From a30cfce4b2bc684ea2015457e9a1ac2efdb933c4 Mon Sep 17 00:00:00 2001
From: Pierre-Paul De Breuck <pierre-paul.debreuck@uclouvain.be>
Date: Wed, 10 Mar 2021 13:04:33 +0100
Subject: [PATCH] Update tutorials + composition only fix (#35)

* introduce 'materials' argument in MODData

* DeBreuck2020 Featurizer: removed GaussianSymmetry alias

* Example notebooks update to v0.1.9

* Update MP_2018.6_small.zip with GaussianSymmFunc naming.

* Composition only fix + test

* nan fix in NMIs

* existing notebooks update

* add composition example notebook

* fixed test nan=>0

* push small_composition moddata

* minor tweak for featurization

Co-authored-by: Matthew Evans <7916000+ml-evs@users.noreply.github.com>

* Raise RuntimeError when NaN in Cross NMI

Co-authored-by: Matthew Evans <7916000+ml-evs@users.noreply.github.com>

* comp only minor tweak

Co-authored-by: Matthew Evans <7916000+ml-evs@users.noreply.github.com>

* Drop features with low entropy (threshold is an argument)

* make get_cross_nmi backward compatible

Co-authored-by: Matthew Evans <7916000+ml-evs@users.noreply.github.com>
---
 example_notebooks/composition_example.ipynb   |  792 ++++++++++
 .../out/MODNet_refractive_index.h5            |  Bin 572504 -> 572504 bytes
 .../out/MODNet_refractive_index.json          |    2 +-
 .../out/MODNet_refractive_index.pkl           |  Bin 81739 -> 81762 bytes
 example_notebooks/predicting_ref_index.ipynb  |   14 +-
 example_notebooks/predicting_vib_thermo.ipynb | 1104 ++++++++++++-
 example_notebooks/training_ref_index.ipynb    | 1397 ++++++-----------
 modnet/featurizers/featurizers.py             |   24 +-
 modnet/featurizers/presets/debreuck_2020.py   |    5 +-
 modnet/preprocessing.py                       |   65 +-
 modnet/tests/conftest.py                      |   16 +-
 modnet/tests/data/MP_2018.6_small.zip         |  Bin 29922 -> 29974 bytes
 .../data/MP_2018.6_small_composition.zip      |  Bin 0 -> 6156 bytes
 modnet/tests/test_preprocessing.py            |   33 +-
 pretrained/vib_thermo.h5                      |  Bin 6159944 -> 6170288 bytes
 pretrained/vib_thermo.json                    |    2 +-
 pretrained/vib_thermo.pkl                     |  Bin 90028 -> 91764 bytes
 17 files changed, 2480 insertions(+), 974 deletions(-)
 create mode 100644 example_notebooks/composition_example.ipynb
 create mode 100644 modnet/tests/data/MP_2018.6_small_composition.zip

diff --git a/example_notebooks/composition_example.ipynb b/example_notebooks/composition_example.ipynb
new file mode 100644
index 00000000..c0cc05a0
--- /dev/null
+++ b/example_notebooks/composition_example.ipynb
@@ -0,0 +1,792 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prediction of experimental band gap\n",
+    "\n",
+    "This notebooks applies MODNet on the matbench experimental band gap data. It is a good example on how MODNet can be used for a composition only task."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matminer.datasets import load_dataset\n",
+    "from modnet.models import MODNetModel\n",
+    "from modnet.preprocessing import MODData\n",
+    "import matplotlib.pyplot as plt \n",
+    "from pymatgen.core import Composition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset import:\n",
+    "The matbench_expt_gap dataset contains measured band gaps for 4604 compositions of inorganic semiconductors from Zhuo et al., JPCL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>gap expt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(Ag, Au, S)</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(Ag, W, Br)</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(Ag, Ge, Pb, S)</td>\n",
+       "      <td>1.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(Ag, Ge, Pb, Se)</td>\n",
+       "      <td>1.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(Ag, B, Br)</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        composition  gap expt\n",
+       "0       (Ag, Au, S)      0.00\n",
+       "1       (Ag, W, Br)      0.00\n",
+       "2   (Ag, Ge, Pb, S)      1.83\n",
+       "3  (Ag, Ge, Pb, Se)      1.51\n",
+       "4       (Ag, B, Br)      0.00"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matminer.datasets import load_dataset\n",
+    "\n",
+    "df = load_dataset(\"matbench_expt_gap\")\n",
+    "df[\"composition\"] = df[\"composition\"].map(Composition) # maps composition to a pymatgen composition object\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gap expt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4604.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.975951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.445034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.812500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>11.700000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          gap expt\n",
+       "count  4604.000000\n",
+       "mean      0.975951\n",
+       "std       1.445034\n",
+       "min       0.000000\n",
+       "25%       0.000000\n",
+       "50%       0.000000\n",
+       "75%       1.812500\n",
+       "max      11.700000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fa3df2efdc0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(facecolor=\"w\")\n",
+    "ax.hist(df.where(df[\"gap expt\"] == 0)[\"gap expt\"], bins=1, density=False, label=\"Zero band gap\")\n",
+    "ax.hist(df.where(df[\"gap expt\"] > 0)[\"gap expt\"], bins=11, density=False, label=\"Non-zero band gap\")\n",
+    "ax.set_ylabel(\"Frequency\")\n",
+    "ax.set_xlabel(\"Band gap (eV)\")\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MODData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "If you use the ChemEnv tool for your research, please consider citing the following reference(s) :\n",
+      "==================================================================================================\n",
+      "David Waroquiers, Xavier Gonze, Gian-Marco Rignanese, Cathrin Welker-Nieuwoudt, Frank Rosowski,\n",
+      "Michael Goebel, Stephan Schenk, Peter Degelmann, Rute Andre, Robert Glaum, and Geoffroy Hautier,\n",
+      "\"Statistical analysis of coordination environments in oxides\",\n",
+      "Chem. Mater., 2017, 29 (19), pp 8346-8360,\n",
+      "DOI: 10.1021/acs.chemmater.7b02766\n",
+      "\n",
+      "2021-03-01 15:52:55,884 - modnet - INFO - Loaded CompositionOnlyFeaturizer featurizer.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This instantiates the MODData\n",
+    "data = MODData(\n",
+    "    materials=df[\"composition\"], # you can provide composition objects to MODData\n",
+    "    targets=df[\"gap expt\"], \n",
+    "    target_names=[\"gap_expt_eV\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-01 15:52:55,891 - modnet - INFO - Computing features, this can take time...\n",
+      "2021-03-01 15:52:55,891 - modnet - INFO - Applying composition featurizers...\n",
+      "2021-03-01 15:52:55,897 - modnet - INFO - Applying featurizers (AtomicOrbitals(), AtomicPackingEfficiency(), BandCenter(), ElementFraction(), ElementProperty(data_source=<matminer.utils.data.MagpieData object at 0x7fa3df4db280>,\n",
+      "                features=['Number', 'MendeleevNumber', 'AtomicWeight',\n",
+      "                          'MeltingT', 'Column', 'Row', 'CovalentRadius',\n",
+      "                          'Electronegativity', 'NsValence', 'NpValence',\n",
+      "                          'NdValence', 'NfValence', 'NValence', 'NsUnfilled',\n",
+      "                          'NpUnfilled', 'NdUnfilled', 'NfUnfilled', 'NUnfilled',\n",
+      "                          'GSvolume_pa', 'GSbandgap', 'GSmagmom',\n",
+      "                          'SpaceGroupNumber'],\n",
+      "                stats=['minimum', 'maximum', 'range', 'mean', 'avg_dev',\n",
+      "                       'mode']), IonProperty(), Miedema(ss_types=['min'], struct_types=['inter', 'amor', 'ss']), Stoichiometry(), TMetalFraction(), ValenceOrbital(), YangSolidSolution()) to column 'composition'.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cad1786701ca43aaa6da448fe717b6b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='MultipleFeaturizer'), FloatProgress(value=0.0, max=4604.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "2021-03-01 16:00:52,973 - modnet - INFO - Data has successfully been featurized!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Featurization of the moddata\n",
+    "# It will automatically apply composition only featurizers\n",
+    "data.featurize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### train-test split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "split = train_test_split(range(100), test_size=0.1, random_state=1234)\n",
+    "train, test = data.split(split)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-01 16:00:53,015 - modnet - INFO - Computing \"self\" MI (i.e. information entropy) of features\n",
+      "2021-03-01 16:00:53,705 - modnet - INFO - Computing cross NMI between all features...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 270/270 [01:31<00:00,  2.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-01 16:02:25,262 - modnet - INFO - Starting target 1/1: gap_expt_eV ...\n",
+      "2021-03-01 16:02:25,262 - modnet - INFO - Computing mutual information between features and target...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-03-01 16:02:26,142 - modnet - INFO - Computing optimal features...\n",
+      "2021-03-01 16:02:30,027 - modnet - INFO - Selected 50/195 features...\n",
+      "2021-03-01 16:02:33,175 - modnet - INFO - Selected 100/195 features...\n",
+      "2021-03-01 16:02:35,527 - modnet - INFO - Selected 150/195 features...\n",
+      "2021-03-01 16:02:37,074 - modnet - INFO - Done with target 1/1: gap_expt_eV.\n",
+      "2021-03-01 16:02:37,074 - modnet - INFO - Merging all features...\n",
+      "2021-03-01 16:02:37,075 - modnet - INFO - Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train.feature_selection(n=-1)\n",
+    "# if you want to use precomputed cross_nmi of the MP. This saves time :\n",
+    "# data.feature_selection(n=-1, use_precomputed_cross_nmi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MODNet model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = MODNetModel([[['gap_expt_eV']]],\n",
+    "                    weights={'gap_expt_eV':1},\n",
+    "                    num_neurons = [[256], [128], [16], [16]],\n",
+    "                    n_feat = 150,\n",
+    "                    act =  \"elu\"\n",
+    "                   )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "2/2 [==============================] - 0s 53ms/step - loss: 1.1403 - mae: 1.1403 - val_loss: 1.1806 - val_mae: 1.1806\n",
+      "Epoch 2/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.9577 - mae: 0.9577 - val_loss: 0.8098 - val_mae: 0.8098\n",
+      "Epoch 3/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.8267 - mae: 0.8267 - val_loss: 0.5956 - val_mae: 0.5956\n",
+      "Epoch 4/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.7825 - mae: 0.7825 - val_loss: 0.5075 - val_mae: 0.5075\n",
+      "Epoch 5/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.7321 - mae: 0.7321 - val_loss: 0.4663 - val_mae: 0.4663\n",
+      "Epoch 6/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.6460 - mae: 0.6460 - val_loss: 0.4526 - val_mae: 0.4526\n",
+      "Epoch 7/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.6106 - mae: 0.6106 - val_loss: 0.4397 - val_mae: 0.4397\n",
+      "Epoch 8/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.6307 - mae: 0.6307 - val_loss: 0.4066 - val_mae: 0.4066\n",
+      "Epoch 9/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.6335 - mae: 0.6335 - val_loss: 0.3399 - val_mae: 0.3399\n",
+      "Epoch 10/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.6024 - mae: 0.6024 - val_loss: 0.3611 - val_mae: 0.3611\n",
+      "Epoch 11/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.5935 - mae: 0.5935 - val_loss: 0.3748 - val_mae: 0.3748\n",
+      "Epoch 12/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.6024 - mae: 0.6024 - val_loss: 0.3613 - val_mae: 0.3613\n",
+      "Epoch 13/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.5872 - mae: 0.5872 - val_loss: 0.3380 - val_mae: 0.3380\n",
+      "Epoch 14/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.5521 - mae: 0.5521 - val_loss: 0.3084 - val_mae: 0.3084\n",
+      "Epoch 15/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.5124 - mae: 0.5124 - val_loss: 0.3470 - val_mae: 0.3470\n",
+      "Epoch 16/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.5071 - mae: 0.5071 - val_loss: 0.3885 - val_mae: 0.3885\n",
+      "Epoch 17/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.5099 - mae: 0.5099 - val_loss: 0.3749 - val_mae: 0.3749\n",
+      "Epoch 18/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.5000 - mae: 0.5000 - val_loss: 0.3229 - val_mae: 0.3229\n",
+      "Epoch 19/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.4938 - mae: 0.4938 - val_loss: 0.2932 - val_mae: 0.2932\n",
+      "Epoch 20/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4896 - mae: 0.4896 - val_loss: 0.2916 - val_mae: 0.2916\n",
+      "Epoch 21/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4728 - mae: 0.4728 - val_loss: 0.3292 - val_mae: 0.3292\n",
+      "Epoch 22/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4632 - mae: 0.4632 - val_loss: 0.3369 - val_mae: 0.3369\n",
+      "Epoch 23/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4573 - mae: 0.4573 - val_loss: 0.3149 - val_mae: 0.3149\n",
+      "Epoch 24/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4447 - mae: 0.4447 - val_loss: 0.3194 - val_mae: 0.3194\n",
+      "Epoch 25/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4426 - mae: 0.4426 - val_loss: 0.3206 - val_mae: 0.3206\n",
+      "Epoch 26/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4319 - mae: 0.4319 - val_loss: 0.3186 - val_mae: 0.3186\n",
+      "Epoch 27/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4270 - mae: 0.4270 - val_loss: 0.3013 - val_mae: 0.3013\n",
+      "Epoch 28/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4189 - mae: 0.4189 - val_loss: 0.2897 - val_mae: 0.2897\n",
+      "Epoch 29/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4151 - mae: 0.4151 - val_loss: 0.3195 - val_mae: 0.3195\n",
+      "Epoch 30/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4068 - mae: 0.4068 - val_loss: 0.3268 - val_mae: 0.3268\n",
+      "Epoch 31/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4006 - mae: 0.4006 - val_loss: 0.3174 - val_mae: 0.3174\n",
+      "Epoch 32/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3943 - mae: 0.3943 - val_loss: 0.2972 - val_mae: 0.2972\n",
+      "Epoch 33/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3893 - mae: 0.3893 - val_loss: 0.2909 - val_mae: 0.2909\n",
+      "Epoch 34/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4032 - mae: 0.4032 - val_loss: 0.3035 - val_mae: 0.3035\n",
+      "Epoch 35/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3910 - mae: 0.3910 - val_loss: 0.3518 - val_mae: 0.3518\n",
+      "Epoch 36/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4157 - mae: 0.4157 - val_loss: 0.3268 - val_mae: 0.3268\n",
+      "Epoch 37/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3787 - mae: 0.3787 - val_loss: 0.2772 - val_mae: 0.2772\n",
+      "Epoch 38/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.4021 - mae: 0.4021 - val_loss: 0.2739 - val_mae: 0.2739\n",
+      "Epoch 39/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3857 - mae: 0.3857 - val_loss: 0.3349 - val_mae: 0.3349\n",
+      "Epoch 40/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3699 - mae: 0.3699 - val_loss: 0.3300 - val_mae: 0.3300\n",
+      "Epoch 41/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3686 - mae: 0.3686 - val_loss: 0.2848 - val_mae: 0.2848\n",
+      "Epoch 42/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3618 - mae: 0.3618 - val_loss: 0.2708 - val_mae: 0.2708\n",
+      "Epoch 43/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3633 - mae: 0.3633 - val_loss: 0.2664 - val_mae: 0.2664\n",
+      "Epoch 44/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3563 - mae: 0.3563 - val_loss: 0.2749 - val_mae: 0.2749\n",
+      "Epoch 45/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3504 - mae: 0.3504 - val_loss: 0.3002 - val_mae: 0.3002\n",
+      "Epoch 46/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3551 - mae: 0.3551 - val_loss: 0.2617 - val_mae: 0.2617\n",
+      "Epoch 47/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3462 - mae: 0.3462 - val_loss: 0.2476 - val_mae: 0.2476\n",
+      "Epoch 48/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3529 - mae: 0.3529 - val_loss: 0.2612 - val_mae: 0.2612\n",
+      "Epoch 49/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3468 - mae: 0.3468 - val_loss: 0.2711 - val_mae: 0.2711\n",
+      "Epoch 50/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3432 - mae: 0.3432 - val_loss: 0.2457 - val_mae: 0.2457\n",
+      "Epoch 51/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3377 - mae: 0.3377 - val_loss: 0.2663 - val_mae: 0.2663\n",
+      "Epoch 52/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3370 - mae: 0.3370 - val_loss: 0.2847 - val_mae: 0.2847\n",
+      "Epoch 53/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3323 - mae: 0.3323 - val_loss: 0.2692 - val_mae: 0.2692\n",
+      "Epoch 54/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3240 - mae: 0.3240 - val_loss: 0.2706 - val_mae: 0.2706\n",
+      "Epoch 55/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3310 - mae: 0.3310 - val_loss: 0.2606 - val_mae: 0.2606\n",
+      "Epoch 56/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3298 - mae: 0.3298 - val_loss: 0.2561 - val_mae: 0.2561\n",
+      "Epoch 57/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.3256 - mae: 0.3256 - val_loss: 0.2406 - val_mae: 0.2406\n",
+      "Epoch 58/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3221 - mae: 0.3221 - val_loss: 0.2469 - val_mae: 0.2469\n",
+      "Epoch 59/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3148 - mae: 0.3148 - val_loss: 0.2829 - val_mae: 0.2829\n",
+      "Epoch 60/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3173 - mae: 0.3173 - val_loss: 0.2982 - val_mae: 0.2982\n",
+      "Epoch 61/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3278 - mae: 0.3278 - val_loss: 0.2672 - val_mae: 0.2672\n",
+      "Epoch 62/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3147 - mae: 0.3147 - val_loss: 0.2564 - val_mae: 0.2564\n",
+      "Epoch 63/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3023 - mae: 0.3023 - val_loss: 0.2661 - val_mae: 0.2661\n",
+      "Epoch 64/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3096 - mae: 0.3096 - val_loss: 0.2566 - val_mae: 0.2566\n",
+      "Epoch 65/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3074 - mae: 0.3074 - val_loss: 0.2392 - val_mae: 0.2392\n",
+      "Epoch 66/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.3029 - mae: 0.3029 - val_loss: 0.2391 - val_mae: 0.2391\n",
+      "Epoch 67/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2964 - mae: 0.2964 - val_loss: 0.2274 - val_mae: 0.2274\n",
+      "Epoch 68/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2932 - mae: 0.2932 - val_loss: 0.2589 - val_mae: 0.2589\n",
+      "Epoch 69/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.3011 - mae: 0.3011 - val_loss: 0.2981 - val_mae: 0.2981\n",
+      "Epoch 70/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2972 - mae: 0.2972 - val_loss: 0.2714 - val_mae: 0.2714\n",
+      "Epoch 71/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2911 - mae: 0.2911 - val_loss: 0.2824 - val_mae: 0.2824\n",
+      "Epoch 72/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2844 - mae: 0.2844 - val_loss: 0.2689 - val_mae: 0.2689\n",
+      "Epoch 73/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2867 - mae: 0.2867 - val_loss: 0.2735 - val_mae: 0.2735\n",
+      "Epoch 74/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2851 - mae: 0.2851 - val_loss: 0.2878 - val_mae: 0.2878\n",
+      "Epoch 75/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2804 - mae: 0.2804 - val_loss: 0.2580 - val_mae: 0.2580\n",
+      "Epoch 76/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2808 - mae: 0.2808 - val_loss: 0.2448 - val_mae: 0.2448\n",
+      "Epoch 77/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2887 - mae: 0.2887 - val_loss: 0.2720 - val_mae: 0.2720\n",
+      "Epoch 78/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2816 - mae: 0.2816 - val_loss: 0.2942 - val_mae: 0.2942\n",
+      "Epoch 79/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.2792 - mae: 0.2792 - val_loss: 0.2699 - val_mae: 0.2699\n",
+      "Epoch 80/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2739 - mae: 0.2739 - val_loss: 0.2697 - val_mae: 0.2697\n",
+      "Epoch 81/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2687 - mae: 0.2687 - val_loss: 0.3203 - val_mae: 0.3203\n",
+      "Epoch 82/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2759 - mae: 0.2759 - val_loss: 0.2798 - val_mae: 0.2798\n",
+      "Epoch 83/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2658 - mae: 0.2658 - val_loss: 0.2865 - val_mae: 0.2865\n",
+      "Epoch 84/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2585 - mae: 0.2585 - val_loss: 0.3228 - val_mae: 0.3228\n",
+      "Epoch 85/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.2633 - mae: 0.2633 - val_loss: 0.2907 - val_mae: 0.2907\n",
+      "Epoch 86/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2666 - mae: 0.2666 - val_loss: 0.3036 - val_mae: 0.3036\n",
+      "Epoch 87/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2562 - mae: 0.2562 - val_loss: 0.3094 - val_mae: 0.3094\n",
+      "Epoch 88/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2571 - mae: 0.2571 - val_loss: 0.2702 - val_mae: 0.2702\n",
+      "Epoch 89/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2718 - mae: 0.2718 - val_loss: 0.2917 - val_mae: 0.2917\n",
+      "Epoch 90/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2505 - mae: 0.2505 - val_loss: 0.2882 - val_mae: 0.2882\n",
+      "Epoch 91/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2457 - mae: 0.2457 - val_loss: 0.3332 - val_mae: 0.3332\n",
+      "Epoch 92/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2543 - mae: 0.2543 - val_loss: 0.3210 - val_mae: 0.3210\n",
+      "Epoch 93/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2558 - mae: 0.2558 - val_loss: 0.2907 - val_mae: 0.2907\n",
+      "Epoch 94/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2489 - mae: 0.2489 - val_loss: 0.3592 - val_mae: 0.3592\n",
+      "Epoch 95/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2620 - mae: 0.2620 - val_loss: 0.3163 - val_mae: 0.3163\n",
+      "Epoch 96/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2490 - mae: 0.2490 - val_loss: 0.2647 - val_mae: 0.2647\n",
+      "Epoch 97/100\n",
+      "2/2 [==============================] - 0s 7ms/step - loss: 0.2740 - mae: 0.2740 - val_loss: 0.3148 - val_mae: 0.3148\n",
+      "Epoch 98/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2389 - mae: 0.2389 - val_loss: 0.3308 - val_mae: 0.3308\n",
+      "Epoch 99/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2458 - mae: 0.2458 - val_loss: 0.3030 - val_mae: 0.3030\n",
+      "Epoch 100/100\n",
+      "2/2 [==============================] - 0s 8ms/step - loss: 0.2369 - mae: 0.2369 - val_loss: 0.3392 - val_mae: 0.3392\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(train,\n",
+    "          val_fraction = 0.1,\n",
+    "          lr = 0.0002,\n",
+    "          batch_size = 64,\n",
+    "          loss = 'mae',\n",
+    "          epochs = 100,\n",
+    "          verbose = 1,\n",
+    "         )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predicting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gap_expt_eV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>id40</th>\n",
+       "      <td>-0.508559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id35</th>\n",
+       "      <td>2.871215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id81</th>\n",
+       "      <td>-0.570688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id61</th>\n",
+       "      <td>1.950733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id98</th>\n",
+       "      <td>2.004308</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      gap_expt_eV\n",
+       "id40    -0.508559\n",
+       "id35     2.871215\n",
+       "id81    -0.570688\n",
+       "id61     1.950733\n",
+       "id98     2.004308"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred = model.predict(test)\n",
+    "pred.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mae: 0.3859002037048341\n"
+     ]
+    }
+   ],
+   "source": [
+    "mae_test = np.absolute(pred.values-test.df_targets.values).mean()\n",
+    "print(f'mae: {mae_test}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:modnet-develop]",
+   "language": "python",
+   "name": "conda-env-modnet-develop-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/example_notebooks/out/MODNet_refractive_index.h5 b/example_notebooks/out/MODNet_refractive_index.h5
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diff --git a/example_notebooks/out/MODNet_refractive_index.json b/example_notebooks/out/MODNet_refractive_index.json
index e84e5c84..2ab3afa5 100644
--- a/example_notebooks/out/MODNet_refractive_index.json
+++ b/example_notebooks/out/MODNet_refractive_index.json
@@ -1 +1 @@
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\ No newline at end of file
+{"class_name": "Functional", "config": {"name": "functional_3", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 1000], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_2"}, "name": "input_2", "inbound_nodes": []}, {"class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 128, "activation": "elu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_3", "inbound_nodes": [[["input_2", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 64, "activation": "elu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_4", "inbound_nodes": [[["dense_3", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 32, "activation": "elu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_5", "inbound_nodes": [[["dense_4", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "refractive_index", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "refractive_index", "inbound_nodes": [[["dense_5", 0, 0, {}]]]}], "input_layers": [["input_2", 0, 0]], "output_layers": [["refractive_index", 0, 0]]}, "keras_version": "2.4.0", "backend": "tensorflow"}
\ No newline at end of file
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diff --git a/example_notebooks/predicting_ref_index.ipynb b/example_notebooks/predicting_ref_index.ipynb
index b49785e1..cfd1b2f8 100644
--- a/example_notebooks/predicting_ref_index.ipynb
+++ b/example_notebooks/predicting_ref_index.ipynb
@@ -30,11 +30,11 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:root:Loading model from ../pretrained/refractive_index(.json/.h5/.pkl)\n",
-      "INFO:root:Loaded `MODNetModel` created with modnet version 0.1.8.\n"
+      "2021-02-24 14:28:00,039 - modnet - INFO - Loading model from ../pretrained/refractive_index(.json/.h5/.pkl)\n",
+      "2021-02-24 14:28:00,118 - modnet - INFO - Loaded `MODNetModel` created with modnet version 0.1.8.\n"
      ]
     }
    ],
@@ -57,10 +57,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:root:Loaded <modnet.preprocessing.MODData object at 0x7f86af24eb50> object, created with modnet version <=0.1.7\n"
+      "2021-02-24 14:28:33,168 - modnet - INFO - Loaded <modnet.preprocessing.MODData object at 0x7f98b3463af0> object, created with modnet version <=0.1.7\n"
      ]
     }
    ],
@@ -280,9 +280,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:modnet]",
+   "display_name": "Python [conda env:modnet-develop]",
    "language": "python",
-   "name": "conda-env-modnet-py"
+   "name": "conda-env-modnet-develop-py"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/example_notebooks/predicting_vib_thermo.ipynb b/example_notebooks/predicting_vib_thermo.ipynb
index 06ac48fd..84859d6f 100644
--- a/example_notebooks/predicting_vib_thermo.ipynb
+++ b/example_notebooks/predicting_vib_thermo.ipynb
@@ -1,16 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# \n",
-    "# Note:\n",
-    "### This example notebook was written for modnet 0.1 and will not work as it !\n",
-    "### Please use the two other \"ref_index\" notebooks as tutorials, and this as an inspiration for multi-target learning.\n",
-    "### An update will follow..."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -24,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,20 +31,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-02-24 14:28:06,716 - modnet - INFO - Loading model from ../pretrained/vib_thermo(.json/.h5/.pkl)\n",
+      "2021-02-24 14:28:16,072 - modnet - INFO - Loaded `MODNetModel` created with modnet version <=0.1.7.\n"
+     ]
+    }
+   ],
    "source": [
     "model = MODNetModel.load('../pretrained/vib_thermo')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -67,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -308,7 +299,7 @@
        "[5 rows x 642 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -322,31 +313,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "If you use the ChemEnv tool for your research, please consider citing the following reference(s) :\n",
+      "==================================================================================================\n",
+      "David Waroquiers, Xavier Gonze, Gian-Marco Rignanese, Cathrin Welker-Nieuwoudt, Frank Rosowski,\n",
+      "Michael Goebel, Stephan Schenk, Peter Degelmann, Rute Andre, Robert Glaum, and Geoffroy Hautier,\n",
+      "\"Statistical analysis of coordination environments in oxides\",\n",
+      "Chem. Mater., 2017, 29 (19), pp 8346-8360,\n",
+      "DOI: 10.1021/acs.chemmater.7b02766\n",
+      "\n",
+      "2021-02-24 14:28:16,406 - modnet - INFO - Loaded DeBreuck2020Featurizer featurizer.\n"
+     ]
+    }
+   ],
+   "source": [
+    "md = MODData(df['structure'],\n",
+    "             structure_ids = df.index\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "md = MODData(df['structure'],mpids = df.index)"
+    "#### Here we featurize using the database. At the end of this notebook a second option is given without using the database. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computing features, this can take time...\n",
-      "Fast featurization on, retrieving from database...\n",
-      "Retrieved features for 5 out of 5 materials\n",
-      "Data has successfully been featurized!\n"
+      "2021-02-24 14:28:16,412 - modnet - INFO - Computing features, this can take time...\n",
+      "2021-02-24 14:28:16,413 - modnet - INFO - Fast featurization on, retrieving from database...\n",
+      "2021-02-24 14:28:20,102 - modnet - INFO - Retrieved features for 5 out of 5 materials\n",
+      "2021-02-24 14:28:20,500 - modnet - INFO - Data has successfully been featurized!\n"
      ]
     }
    ],
    "source": [
-    "md.featurize(fast=True)"
+    "md.featurize(fast=True,\n",
+    "             db_file='../modnet/data/feature_database.pkl'\n",
+    "            )"
    ]
   },
   {
@@ -358,13 +377,229 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "df_predictions = model.predict(md)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>S_5_atom</th>\n",
+       "      <th>S_10_atom</th>\n",
+       "      <th>S_15_atom</th>\n",
+       "      <th>S_20_atom</th>\n",
+       "      <th>S_25_atom</th>\n",
+       "      <th>S_30_atom</th>\n",
+       "      <th>S_35_atom</th>\n",
+       "      <th>S_40_atom</th>\n",
+       "      <th>S_45_atom</th>\n",
+       "      <th>S_50_atom</th>\n",
+       "      <th>...</th>\n",
+       "      <th>H_760_atom</th>\n",
+       "      <th>H_765_atom</th>\n",
+       "      <th>H_770_atom</th>\n",
+       "      <th>H_775_atom</th>\n",
+       "      <th>H_780_atom</th>\n",
+       "      <th>H_785_atom</th>\n",
+       "      <th>H_790_atom</th>\n",
+       "      <th>H_795_atom</th>\n",
+       "      <th>H_800_atom</th>\n",
+       "      <th>eform</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mp-14363</th>\n",
+       "      <td>0.014347</td>\n",
+       "      <td>0.018734</td>\n",
+       "      <td>0.238954</td>\n",
+       "      <td>0.606634</td>\n",
+       "      <td>1.057049</td>\n",
+       "      <td>1.684448</td>\n",
+       "      <td>2.379784</td>\n",
+       "      <td>3.057159</td>\n",
+       "      <td>3.625864</td>\n",
+       "      <td>4.372535</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-17274.916016</td>\n",
+       "      <td>-17516.820312</td>\n",
+       "      <td>-17742.035156</td>\n",
+       "      <td>-17984.978516</td>\n",
+       "      <td>-18219.955078</td>\n",
+       "      <td>-18462.693359</td>\n",
+       "      <td>-18669.978516</td>\n",
+       "      <td>-18922.746094</td>\n",
+       "      <td>-19187.947266</td>\n",
+       "      <td>-1.895996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mp-988</th>\n",
+       "      <td>-0.010973</td>\n",
+       "      <td>-0.001627</td>\n",
+       "      <td>-0.016520</td>\n",
+       "      <td>0.028203</td>\n",
+       "      <td>0.086789</td>\n",
+       "      <td>0.061554</td>\n",
+       "      <td>0.071316</td>\n",
+       "      <td>0.221528</td>\n",
+       "      <td>0.156996</td>\n",
+       "      <td>0.339230</td>\n",
+       "      <td>...</td>\n",
+       "      <td>714.511047</td>\n",
+       "      <td>561.200562</td>\n",
+       "      <td>504.741852</td>\n",
+       "      <td>382.601746</td>\n",
+       "      <td>291.089142</td>\n",
+       "      <td>157.871490</td>\n",
+       "      <td>112.379631</td>\n",
+       "      <td>-7.474174</td>\n",
+       "      <td>-194.458313</td>\n",
+       "      <td>-0.710409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mp-38487</th>\n",
+       "      <td>-0.008141</td>\n",
+       "      <td>0.007511</td>\n",
+       "      <td>0.027280</td>\n",
+       "      <td>0.079071</td>\n",
+       "      <td>0.160426</td>\n",
+       "      <td>0.249019</td>\n",
+       "      <td>0.380474</td>\n",
+       "      <td>0.536368</td>\n",
+       "      <td>0.630777</td>\n",
+       "      <td>0.908211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-8083.064453</td>\n",
+       "      <td>-8281.423828</td>\n",
+       "      <td>-8404.014648</td>\n",
+       "      <td>-8546.526367</td>\n",
+       "      <td>-8694.868164</td>\n",
+       "      <td>-8875.536133</td>\n",
+       "      <td>-9001.016602</td>\n",
+       "      <td>-9178.028320</td>\n",
+       "      <td>-9363.007812</td>\n",
+       "      <td>-2.205507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mp-559200</th>\n",
+       "      <td>-0.010615</td>\n",
+       "      <td>0.036975</td>\n",
+       "      <td>0.161285</td>\n",
+       "      <td>0.393592</td>\n",
+       "      <td>0.742751</td>\n",
+       "      <td>1.148751</td>\n",
+       "      <td>1.614309</td>\n",
+       "      <td>2.131572</td>\n",
+       "      <td>2.705519</td>\n",
+       "      <td>3.401336</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-21751.392578</td>\n",
+       "      <td>-22014.144531</td>\n",
+       "      <td>-22267.328125</td>\n",
+       "      <td>-22514.515625</td>\n",
+       "      <td>-22766.244141</td>\n",
+       "      <td>-23032.716797</td>\n",
+       "      <td>-23287.316406</td>\n",
+       "      <td>-23562.757812</td>\n",
+       "      <td>-23815.939453</td>\n",
+       "      <td>-0.687132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mp-4661</th>\n",
+       "      <td>0.002966</td>\n",
+       "      <td>0.024075</td>\n",
+       "      <td>0.082043</td>\n",
+       "      <td>0.099225</td>\n",
+       "      <td>0.067827</td>\n",
+       "      <td>0.083071</td>\n",
+       "      <td>0.112224</td>\n",
+       "      <td>0.205507</td>\n",
+       "      <td>0.260845</td>\n",
+       "      <td>0.519051</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-10469.830078</td>\n",
+       "      <td>-10686.641602</td>\n",
+       "      <td>-10830.547852</td>\n",
+       "      <td>-10990.991211</td>\n",
+       "      <td>-11161.797852</td>\n",
+       "      <td>-11356.781250</td>\n",
+       "      <td>-11511.146484</td>\n",
+       "      <td>-11712.181641</td>\n",
+       "      <td>-11910.938477</td>\n",
+       "      <td>-3.540361</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 641 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           S_5_atom  S_10_atom  S_15_atom  S_20_atom  S_25_atom  S_30_atom  \\\n",
+       "mp-14363   0.014347   0.018734   0.238954   0.606634   1.057049   1.684448   \n",
+       "mp-988    -0.010973  -0.001627  -0.016520   0.028203   0.086789   0.061554   \n",
+       "mp-38487  -0.008141   0.007511   0.027280   0.079071   0.160426   0.249019   \n",
+       "mp-559200 -0.010615   0.036975   0.161285   0.393592   0.742751   1.148751   \n",
+       "mp-4661    0.002966   0.024075   0.082043   0.099225   0.067827   0.083071   \n",
+       "\n",
+       "           S_35_atom  S_40_atom  S_45_atom  S_50_atom  ...    H_760_atom  \\\n",
+       "mp-14363    2.379784   3.057159   3.625864   4.372535  ... -17274.916016   \n",
+       "mp-988      0.071316   0.221528   0.156996   0.339230  ...    714.511047   \n",
+       "mp-38487    0.380474   0.536368   0.630777   0.908211  ...  -8083.064453   \n",
+       "mp-559200   1.614309   2.131572   2.705519   3.401336  ... -21751.392578   \n",
+       "mp-4661     0.112224   0.205507   0.260845   0.519051  ... -10469.830078   \n",
+       "\n",
+       "             H_765_atom    H_770_atom    H_775_atom    H_780_atom  \\\n",
+       "mp-14363  -17516.820312 -17742.035156 -17984.978516 -18219.955078   \n",
+       "mp-988       561.200562    504.741852    382.601746    291.089142   \n",
+       "mp-38487   -8281.423828  -8404.014648  -8546.526367  -8694.868164   \n",
+       "mp-559200 -22014.144531 -22267.328125 -22514.515625 -22766.244141   \n",
+       "mp-4661   -10686.641602 -10830.547852 -10990.991211 -11161.797852   \n",
+       "\n",
+       "             H_785_atom    H_790_atom    H_795_atom    H_800_atom     eform  \n",
+       "mp-14363  -18462.693359 -18669.978516 -18922.746094 -19187.947266 -1.895996  \n",
+       "mp-988       157.871490    112.379631     -7.474174   -194.458313 -0.710409  \n",
+       "mp-38487   -8875.536133  -9001.016602  -9178.028320  -9363.007812 -2.205507  \n",
+       "mp-559200 -23032.716797 -23287.316406 -23562.757812 -23815.939453 -0.687132  \n",
+       "mp-4661   -11356.781250 -11511.146484 -11712.181641 -11910.938477 -3.540361  \n",
+       "\n",
+       "[5 rows x 641 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_predictions.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -374,12 +609,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -391,7 +626,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -403,7 +638,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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qN3F9XB4Hs8YxaM61JAfH+Ls80QtJ+AvRh3WV76T8g/vJrP6IEVozL2YQ0y+6iClZ82WMvvhWEv5C9EGdHW3UP72QtMa1JGsrS0NPJ2HuLdw+arS/SxN9hIS/EH2Fy0lX6RaeL43hyVWF3GN3syHqCgYv+Dlzc7L8XZ3oYyT8hejtXE5s216le+n9BHVV83T3w4zIySHxpNcYnx7p7+pEHyXhL0Rv5XLi2PYqXUv/TFhXGYfc6SyJu4vHTz+VcenR/q5O9HES/kL0Qnanmw9Xree0VTdS6k7jo5jfMev0H/HzTAl94RsS/kL0Fi4nru2vUrhrLVfWnE95Uxdrkh7mnFNP5dbBsf6uTvQzEv5C9ALug8voeP9WQtsK6HJnkhBzLr8/awKzcmJlyKboERL+QviRbqmg4Y0biKlYRqM7jgdCfsXk+T/izREJEvqiR0n4C+EnW0ub+PeHu/ld1U7+bf4RyfN+wd3jMjEaJPRFz5PwF+J4cruoWfEEdZve4tymnxMdEsCyOR9x1eQsrCY5rbI4fiT8hThO6vauxvHez0myFVCuh/KrmfFcPHscwVb5b9gbudo7MIYEA9C1ew+2gwfRdjva4fj8FnPtNQC0Ll5M944daLfG5XbidNpxGRWm66+k29WN/b1FuPfm43I6cbuduFxOnIFmyq+ei91lJ/aVpYTsK0N1OzDY7BjsTjoiAlh0yxSSQ5K5cdyNPn9+8qoTooe1tjZz8OVbGFv9FjVE8lb2fZx4zrWMD7H6u7Q+y22346qvxxgRgSEoCHtJCe0rVuDu7PTcOjw/Y352HZa0NDrWr6fxhRchKBB3gAVXoAVngAnH6SfSGWbBsW0XxlUbUdX1GGsasNQ2Y2rvZvETl9NqcTLs1Y0M//jg1+r4cfR7dLq7Oef9BqZu7UYr0ArcCmxm+EnSmwBc/bGLiQc0bu8yraAlCO4dsQGAy0pcDG7W2M0GnIEGHBFGWiI0u+p34dKuHvkdyvn8heghdqebVzaU8NjSvbzo/CWV0ZMZdOH9JMfH+bu0Xkdrjbu9HVdDA87GRpwNDbgaGgmcmIcjJY7mXdtp/9sjuBsaobEZQ3snAAfvWkjF6ARC1+1l/ENLAHCaDDgsBmwWxUuXJHAgUZOzq5nTlncQYNcE2iHADlYn/OIaIxUxilO2uLl0uZu6cKgLV5//XDU+AB0SSKzNQqjTgskagNkSgMkaiNkaiDkgCKspgABTAAHGAKwmq+en0UqAyfPzs3kWo+XzZZ9NW4yWz+9bjBZMBt+2x7/tfP4S/kL4mNaaZZt20P7Jn7izfSG52cncdUomI9Lj/V1aj9JOJ86GBpy1dTjrPDdzziCcw7Joqiik61f34bJ14+7uRtvtYLNz6Kxc9k1LxVRYwVm/X/m1fT5+qoGluQZS6jRXf+yiJVjRHAzNwYrmENiZqWgIU4RoK+EEYgwKJjAghCBzkOdmCiLYHEyQyXM/0BRIoCmQIFMQgcpCkCWYQEswgVgIsAYTaP5iudVoxdjHL28pF3MR4jjZUlzPprce4OLWZ7AqF+mnXsSYGZN6/bBNrTWdGzfh7mhHd3fj7urGbevGkppKyPTpaK2pvP+PdDXWYW9rwdnWiqujnYa8bPYvGE5rewNnXfsa6ittyfcmK14+0UiATXN7kwu7SeGwgD0IHCbY0LGe4tJ8YlQQQacl4owIgcgwiIzAFBVFTkwMYwPDCbWEEnxeMAnmEILNwV+7+brFPBDIb0wIHyhr7OT1d95iVskj/NRwgJqYSQRf9Ci5Mdn+Lu1LtMuFvbQU2/79dO3diy3QROeFc6nvqif8ppsxNbV9af3dY8J5siaMxu5GHnynAzR0WaHLAl0Wxeaacj7ZuYZwSxjBsyNxRobgjgpHxURhioklPC6eO4KjCLOEEXZqGGHWMELNoYRaQgmzhnGdMeCLN8aL/fALGcCk20eIY9Buc/Lv5Yd4ak0Rzxr/QK6lAtPce7COv6zHL5motcbd0fmlESn2kmIcLc20NVTR0VhLp7ZRcOFkajtryXzsIzLWl2K2uwFwGmBbtuKv53m6NjKrNWjAaiEoJIKgkAiCw2IIC48lKiCKyIBIIq2RRFgjiAiIIMIaQaQ1klBLaJ/vHumvpNtHCB9zuTX/W7MJ5/I/817X6Zw2NpdBU58jODYBrCHHtG/tcuGsb8BZXYWjuobQU+aglKLxxZdoWfQh9sYGXM3N0NqO22LkgycuprqjmilPbGD0tuYvajRBUwT8MXMZRmXkIouFhklRdKbH4chOwZydRXRYPA8GxhATGEN0QDTRgdEEmgJ7fTeVOHbHHP5KqQBgFWD17u8/WuvfKqUygdeAaGALcJnW2q6UsgIvAOOBBuACrXXxsdYhxPGycW8hJe/dx+nd72NUmnHzziRjVu4P3o+7uxt7YSHWQYNQFgtNr71Ow5NP4qipBucXw/ve+udZFLvqyNq2l8ENLbQFatozoS0Q2gJdLD/wNokhSVjOyKH83CjCYhKJiE4mNiKZ1KA4lgfFEWmNxPgjaZ2LL/ii5W8DZmut25VSZmCNUmoRcDPwoNb6NaXUY8BVwKPen01a60FKqQuBPwMX+KAOIXpUWUMH6165j7n1z5GnuqhIO52Uc35PRmTG99reVlRE6wf/o/vgATrz9+MqK0e5NWv+cA67I9sJKtrDoIhqatLc1IcZaAiFhjBFZ+1aEiNSaD5rGpWXJpMYnMig4AQSvLc/mUOlpS5+sGMOf+05aNDuvWv23jQwmy8O4TwP/A5P+J/pnQb4D/BPpZTSfeHggxiQuh0uHl1RwGMrC/i9IZ/m6FwCzv0zqSlfv16uu7sb28FDdO/fR+e+vbTu3UnNhbPYm2HE9ul6Tvn3JmojFSWxUDoFSuMM7G/4kGhjGmmThtF48lwyQpKZFppCckgySSFJBJoC/fCsRX/nkz5/pZQRT9fOIOBfQAHQrLV2elcpB5K908lAGYDW2qmUasHTNVT/lX1eC1wLkJaW5osyhfhBtNZ8vLuSfe/9naUdmcwZNZVpc58jKSoUlEK73diLi3EbFZXhbop2ribxur9icHkOqHZZoCQW3tq2n50tRtJjkyl5YBbJMVmkhaYxJyyN9LB04oPi5YCpOO58Ev5aaxeQq5SKAN4Bhvpgn08AT4BntM+x7k+IH+JgTRv/fmcpF1b+iV8Y9nNh7hUkLLyB2iWL2L5xFd27dhJ4oBxLp4OP80w8PQcMbs0FkxWdmUmYcgYRO2gUWZHZ/Do8i/SwdAJMAf5+WkJ8zqejfbTWzUqp5cAUIEIpZfK2/lOACu9qFUAqUK6UMgHheA78CuF3rd0OHl6cj/vTJ7it/S2qWgNYlDyLZQkW9ryzgF/9qYjIdqiKhZ3DAunKycA6Ppc/DptATmQOWT/Kwmw0+/tpCPGdfDHaJxZweIM/EJiD5yDucuA8PCN+Lgfe827yvvf+Ou/yZdLfL/zF1daGs7oaU3YWT29YT8WjjzC1cD9JDTaadRiBQFfKAXaM72JE9AhK7pmJIWcCE5JHc2pgjL/LF/2Q1hpnQze2gmbaDzVgDbQSec5gnz+OL1r+icDz3n5/A/CG1voDpdRe4DWl1H3ANuBp7/pPAy8qpQ4BjcCFPqhBiO+lbcUK2hYtoru4iO7iYlRLGw6zgctvDsRpsHGJwUVrmInqUTEEjJ9M4sSZzBg8hXMk6EUPcjZ2Yytspim/mpKiYhLaw7BiZk9QJWNHjumRx/TFaJ+dwNgjzC8EJh5hfjdw/rE+rhDfh62wiPZlSwleeB47Ow5QvexZEpdtpTjaTVWmpjrSQEeoiTNaGxlBIBMf+pD08HQZOil6lLPFhq2gmdYDdRwsOEhlVx1VhmZaDJ6zlZ4xeQ6jpo4lxDwKq7VnTv0t3/AV/YrWmq5t22ldspimxR9DWSUAf6n4F5sznFgzDAz53SjiyCRy305u7FhPrHbhSL2YgNm3Q0Sqn5+B6I9cbXZsBc00H6ihqKCIwFYDcTqcloBulrEDa4CF1KQUJuRkkZ6eTlJSEkajkQiCeqwmCX/RbzR3N7Nx5auk/vwRnAbYna7YcoqBunEZDBs+ncsTJ5NoHc5fF5XSkr+Iv1nW0Jh9NsYFv8YYlenv8kU/4mq3YytsobugmYMHDlDWVkOVoZEGg+crUSMzhzDytFkkxQfxk9qRxMfHYzAYjmuNEv6iTyvauoKS556grLuKv01rxK3dzL4wnIgp0xk/aDq3JU4mLigOp9PFivee5o0d7/KpnsdNp1yEe+SlxMZI6Itj5+pwYC9qoSm/hpKCIjpbOhjiSkJZjKyz7KPN0kVKfBKjh0wgKzvr85Y9QGJiol9qlvAXfYpbu9lZuZW97zxL+AdrySruJtIIRRMjuWrkVZyYeiIjYkZgUF+0ovYfyKfpzRs52bGejMDhXHnt70mJDvXjsxB9nbvTga2oFVthMwX5hzjUUkq1aqbJ0AFASEgwUy+ehzUllEubcggPD8ds7l1DgCX8Ra+ntWZ/7W4WlXzMotKPmflhBeeu1bREB1D+o5MY+qOfcWXK8K9t19FtZ+Urf2F6yT/JUC72jryVYWffgZJx+OIHcnc76S5opm5/BcWFRVS21nKCYwhGk4myiHoOWWpIiU9mbM5EMrIySUpKwmTyxGtMTO8cKSbhL3qtoroDbPzgaWxLljN0TxvbzjaTM206Ey6/iOiLExl64iko45FPi7B8fy1Pv/0hz9v+RlHoOOIvfozhSTnH+RmIvsrd7cRW7GnZlx8oYVvDfqoNzXQoGwABViszzk4laXQGMc7xnG42f96N01dI+Itepbm7mQ93vIHh4WcZvKeZ0TawBRjpnDyaB868mdjcSd+6fV1zO2+98Rz3F2YyKC6T/FPeYfj4WT1+YRXRt7ltTroKm6nYU0JJUREVrTUMc6WQrKJxxrupDmglLSmdzKHZpGdlEBsb+/kB2r562g4Jf+F3Wmu27l7Mqg1v8JJ1Gw6njX+Wm+iYOpq4My9myImnYrBYvnMfy1YuJXnFLfyUYkJOeI3z50/DaupbrTFxfLhtLuwlnpZ9y8E6FtdtoEa1YFeec1GGBYVgmZBA0ow8ks0GxjC73333Q8Jf+E1TRwOr3n4Exzv/Y+i+DiaGKroevoRzh5xHzhU53/s/W1VjK+tf+DULml6kwxhG1dynuXTSqT1cvehL3HYX7YcaKNl1iJKSEiraa4lyhzDFPQRrSgiOMMXwhKFkDh9EelYGERER/i65x0n4i+Nue+12Pn3l74x6dQs5LZqOEBPN585k9I9vZmrW9++X11rz6oZSchZdwNlqHwUJ88i47N9EhkT3YPWiL3DbXXQXNeMobsNW2MKHVZ9SourQSqOA2JAoYnPSSJo/BYPFyE/J9XfJx52Evzgu3NrN6s1v82rRW3zatZtJbQGMTI5H3XoZ48+8FPUd3TpfVVrXyh3v7GFtYSO3JSwgbcotZE+Ss4YMVJ+FfemuAgqLiihrq6adLhY6p2JNCSU5PZnE0DQyRw4mLSudgIC+2U/vSxL+okfZXXY+WfUsTU89Te62NnInhTD9tjs4++KzCTL/8K+uu92adz9ZyuB1vySDeZx29nVcNHF+v+uPFd/ObXPRWdiIo7gNR1ErOyr3s9F4EIfyXPs4NiSSYWnDiTttAtaQAOYMwJb9d5HwFz2i1d7K/z76B67n/8PYPd2kmhUdp0/n8ht/Q2ByylHts7S6jk0v3s3p7W9iM4Zw64IJRI2Xq7wNBG6bk6Z91RTvPURZeTmV7XXUq1ZOc+aRkpxM/KhURjhMZI/MIWtwNsHBwf4uudeT8Bc+1WZv49ndz/Lyvpe58p02JhcYsF+ygJHX3YH5KL/s4nZrln74BsM23cW5qo6S1DNIu/DvqJA4H1cvegt3t5OGfVXYS9swldspqyzjA/MWAAwYiA+PIS9lLGkzJxKTEEsc8PUrKotvI+EvfKLL2cU7y/6F87EX+WSyi2kT5zLzj+eSkzgKY+jRn0qhvKmT29/aSUBhPvcEBdFw9tukjzjJh5WL3sDd5aRxfxWFuw9SXFFKRWctLYZOcl2ZnJCUS8bUoZzoCCRj1CCSUpN73akS+iIJf3FMHG4H/93wAg3/+jdTtnTiMhuYsPDnDJt17THtVztt7HzzDyzbX8d2fSZ3nXkFyXl3yqkZ+gl3p4Pa3eU0F9URWWPCVtXOi5aVOJQLszKRHBXPuMxxDM0bSVxyAgAz8f3VrAYyCX9xVNzazUdFH7H/wfs4cUUTOW6F+6w5jLjlN5iO8VwmDXuWYXvnRsY4y2gPmsl5V08nNVr6cPsyV4eDqh3FFOw7SHl1BZX2ejqUjUgdwsXJpxA+O535hlOIyU4gKTWlz50qoS+S8Bc/2OaKjfx561/Z37if69zhMG0Cg+/4Pdb09GPbsdNGwWu3k33oWcp0HEvGPsLsM36EwSAjefoae3MXJVsPUV5QwtDWBFx1XSw376bAWEOQIYCU2CTSs9LJGp1DbEoSAGM5xteP+EEk/MX3Vt9Vz/Nv3s2IJ1eSMjeGyy/+E/MunYfJeOwvo5YuB4+//h6/KHqBRYHzGXb5I5ycGOuDqsXx4Gy2UbGjiD27d1PWUEmtqxmXcgOQkTSf6HEZnBSdyvyEEKJio2Vobi8g4S++k8vt4q2dL1P14APM22jDHhnMr6f/jqgsHxx4dbvZvfYDrlkdTG1bCLEnvMVlp87EZDy+VzUS35/Wmo7KZgq3HaC4qIjB7XGEtpgpNlSzybKXWEsEucnDycjJInNMDiFhIQCE+blu8WUS/uJb7arbxcsv3c6prxUxqgUM5y5g6J2/wxgScsz77m4sp/K5KxnZupGxgX/h2usuIDc14tiLFj6ltcZZ20nj/mrWbt9AeXMVDe42UGBAERcfScr0IeSljWRy3BlYA3rmguPCt445/JVSqcALQDyggSe01g8rpaKA14EMoBhYqLVuUp7Pew8D84FO4Aqt9dZjrUP4VouthYe2PsRbB95iQW0QkWFxpP3r7wTn5flk/2Wfvk74kltIdNt4L/VW/nbZjwmyykie3sDtclObX0HR7oOUlpUR3R7I0K5E7DjZG1BAfFA0JyQNIWvEINJHZsuwyz7KFy1/J3CL1nqrUioU2KKUWgxcASzVWt+vlLoDuAO4HTgVGOy9TQIe9f4UvcTSkiV8+ORdOLo7ufSCy7juwusIVtbvPK3y9+F2a/Y8cx2jyl9lr8qm9bR/c+aEyT6oWhwt7XRjr2jHVtTC/zYtprS9mi5lB8CKmdDYIUROGow1I5w7ImfISJx+4pjDX2tdBVR5p9uUUvuAZOBMYJZ3teeBFXjC/0zgBa21BtYrpSKUUone/Qg/are38+Dy35P86H+5er+GCWMYmvdLnx2cq23t5uY3dpBQFMZJMRcy6aoHGB4mQziPN2eXg/KdhRTtO0RJdTmOThun2scCoEPdZEQlk5aeTubowcRlJH5+0RLRv/i0z18plQGMBTYA8YcFejWebiHwvDGUHbZZuXfel8JfKXUtcC1AWpqcv6Wnba7ezAvP38IFb9YS0WUg+qYbib3mGp8F/5bl7/LGqh1sdk7kN2fcwLyJqTLi4zhxtNlwlrZjK25h/d7NbGs/gM170ZJwQzBpcUlEzRyKNTOci0Om+7lacbz4LPyVUiHAW8BNWuvWw/9ja621Ukr/kP1prZ8AngDIy8v7QduK78/usvPP7f/k45XP8sBzTkhLJvvBRwgY/vULoh+NbruTlc/9mpMrHiXMlM01N/yCQfEy7qMntVU3U7z9IKWFxZQ3VFPrbGKh7QSCjQEExwQyOD6TjOwsssfmEBkn1z4YqHwS/kopM57gf1lr/bZ3ds1n3TlKqUSg1ju/Akg9bPMU7zxxnOU35nPfx7ex3V7I+VPOJ25ILtEnnYLBR+c6P1BaSfULVzHXuZa9USeSffVzWIMl+H3J7XJTc7AcY50TU4Wd/QUH+MThGT+htCLWGsHohKFETRlKzJAkUszShSM8fDHaRwFPA/u01g8ctuh94HLgfu/P9w6bf4NS6jU8B3pbpL//+NJa88Ku5yh47AFuXe2k44E7OGHK5T7d/yur9zFx6flMVZUUjL2D4WfcIRdR9wFHt51DW/dTeqCIippKqrsasONkqmMIIwIzSU5JZqrRQMbQLNJGZcuwS/GNfNHynwpcBuxSSm33zvsVntB/Qyl1FVACLPQu+xDPMM9DeIZ6XumDGsT31OHo4PeL72DsI0u5qFhjPWkmI/LO8Nn+m9pt/PKtnSzZV8tD8bOJm3sm2cNP9tn+B5qWuiYKt+VjbHKR0BxKW1kjr5tWgIYoYyiDI9NJTUtjcO5QojLiUErJSRLE9+KL0T5rgG9q0n3tK6DeUT7XH+vjih+usKWQ375zPZc9VUJysyLh9/cScd65Pjvwum/tBxgX302L4wp+fdqZnDlVrrD1QzlbbGxfvYnCwiIqmqtpcXcAkO6OZX7cCURPSuWS0HNIHplJUMzRnypbCPmG7wCxuGQxd6+5m9O3uEjpCiD9yX8RPGWKT/btqjtEyWs3M6xhJdUqlj+fPoisSZk+2Xd/Zuu2Ubq7gNL8YtrrW5nYlYWr2cYmy2aaVSdJQTHkJgwnY2g2qaOzMQV6vkwV4d+yRT8h4d/POd1O/rHtH7y07WmGJI7mqj/9nZgWN+bkZJ/sv+Oje7Guf5g4beK/cdcw6/LfkhAiLdKv0lqj7S7spW1sXr+RbaV7abS3oL0fjCJVCFOzRxMyLZmLEocQlh6DwSQHZ0XPkfDvxxq7G7lt1W2YF6/jydVWhrxyL8EhiXCsp+XRGpRi5YE6tm2oJdk9Heuc33L6tFzp5vFyOBxUHCyleI/nmrMVrTWc0z2RIG2l09hIUKCF7IRRpGWmkTZqEKFJEfK7E8eVhH8/tadhDzct+zlTl9VywXI3QRNHERDjg2vedrfgfusa3tczuGl3JkPiF/KvS8YyKG5gt/Zbm1qg0YGqspG/Zx+LqtbiwnNK4zAdREZwIoHjEokZksTJaVMwBMh/PeFf8grsh1aUreD2Fb/kqiUwfYODsPnzSbz/T8d+bp66AzheuQjVVMQGRwoXT5rJb04bToB5YJ3rxel0UnqwmNJ9hZSXVVDVUkuHu4vpjmEMcSURGmZiVORg0tLTyBg5iMjMOJRRWvWid5Hw72de3/86f9z4R368I4rpG6qJuurHxN1yC+pYz8+Svwjnm1fR5jTyC/1rzlt4AaePSfJN0b2Y1prG+gaKdxdgbVckdYTRXFLPC11LAAjVgSQGRJEUl0jOsKEkjkzFGGZliJ/rFuK7SPj3E27t5uGtD/PM7meYkTKDq866F9fs9YQvWHDM+3ZW7sT46kXsc6fz54jfcO9lc8mKPfbz+fdWrlY7a5aspLi0hKqWWrq15wyXma44ogLGE54Wxbkh80gelkHkoDiUHJgVfZCEfz9gd9m5+9O72bL1Q/6+OYmZ//4D1rAIONbg15rqVhs3vt9Bkv06Qsedy1Nnju033TxOp5Pq8kpK9xdTXlyGq8POLMcIXM029lh2Y1MOMgITSI5LJG1QJokj0zBHBqKUQs6II/o6Cf8+rsXWwk3Lb6Jx+yYeeMdKAK3oiioIizi2HTcU0PryFdzSfCm7nelcfP4NnDXWN8ND/UFrTVNjE6HuAOzl7SxZu4wddfm48ZwzMFBbSDRGYxkUimVqElckjyQwNRwl58IR/ZSEfx9W2V7Jz5b8jMhthfzxXQPWmEhSn3wSa9axfcHKVbgaxysX43K4SQpxcs/l0xgU17e6eWw2G2UHiynZX0R5Rbmn+8Zt59LuGQRgJsJiJjdsMEmJSaTmZBA9JBFTmJwHRwwcEv59VH5jPj9d8lNG7mzjurddBA4dSurjj2GKjT2m/bZufIWgD/+Pcnccbw55gHvPP4VAS+/u5nE6nVSXVVK2r5h0YxzWes32oj2sdu4GIEIHkWFNIDk2kagRgwjNiiY5LghlkBE4YuCS8O+Ddtbt5KdLfkqQKYgbL/0Hwc73ib/7bowhx3ZVrPw17zBkyXVs0MOonPcUd54w0kcV+5bb7qL+YBVr1q6huqGG+u7mz7tvZtlHMCQig0FpWcSGpZA2LJOwzBgMvfwNTIjjTcK/j9lcvZnrl/yMOflWrrv1WZLDU+H+YztHj9aax1cV8sDHJm4KuZLZP7qLSSnH9gnCF5xOJ9UV1ZTnF1NZUk51Qy2DSWJYazwd7m72Ww8RawgnN3wISUmJpAzOICYnEVPIsV9rWIj+TsK/D1lbsZablt3IT5eamLK+ntCpe2Be6ndv+C1amhrZ+fTPeLr+dE4eNZTLzv0roQFmH1X8/XlG3lRhr+0g1hFKd3krj+/7D05cAFi0iVhDOMHRQYSOSyUqOYTbUqZjCvfNhWeEGGgk/PuI5aXLuXXFzdy4NJCJG5qIvuZqQufOPaZ97svPx/TaQqa4S7l/0hxmnzXuuJ1fxtVmZ8faLRQXl1DdWEO9rQU3bhJdESxwjMcQamZy5EjC4yJJyU4jNicJY7hVzn8jhI9I+PcBHxV9xK9W3cHNy0MYv6GB6GuuIfbmXxx1EGqt+XDJEsatuZYw1UnR3Gc56YSzfFu093HaWlqpPFhGVXEFNdU12Dq7meMcjbvNwVbzNmoNrcQaw8mNyCEpMZGU7HRP0IdZ6P/fHxbCfyT8e7n3Dr3Hb9b+hpPcQ8jbdoCon/yE2Jt+ftTB32l38vxLz3NFyR10mcJwXrqIwZnjjrlOl8tFbWkVlQfLGByQirOmk2WH1rLbVvz5OsHaSowlAuugCCxJoZwXk0NoehTGoOPfzSTEQCfh34u9vv917ttwH1MSp3DfiQ9hml2HOS3tqIO/oK6d617aQnVtKDNTT2HoJX/FEP7D29fuTgeO2k5K9hex4+BualvraXS0fn4WywtsJxAeHEZWZAoxgbEkpCaRNDiV0NQolFG+NCVEbyDh30u9vO9l/rzhT/x2dQIz58wiyBwE6Ud/ddaNn7xK96eP0Wy8nX/9+ESGD174retrrelu7KB0XxG1FdXU19VT39pIk72V2bYRJOhIqg01HDIXE2OJYEzMEBISEkjMTCFhcDLmsADpthGiF5Pw74Ve3f8qf97wJ363KoFhaytgTP1R78ve1cH2Z/6PiXVvUWzK4P0rhpCQ+sUwTkeXnbrCKmpLq6mvqaWhqZFBJJLQEkq1rZ7/WrcAYNZGIs1hpEckETk4neisFOLixjMt8hwMx3rGUCHEcSfh38u8kf8Gf/n0D/xhaQyDtlYQ87OfEfN/NxzVvmoPbML22pWMdbWwKux6IoadQenKg9Q4K0iyR9BS38TLthVopT/fJggriZFRBI3JJDM6kQtIJSE7hfDEKAl5IfoRn4S/UuoZ4DSgVms90jsvCngdyACKgYVa6ybl6bB+GJgPdAJXaK23+qKOvu7NA2/yh7X38tf/hpO6v4a4X95K9FVXfes22q1xtdtprWqksaqe5vomzDZFQEsI1rKDvGu+jFbVjbtNw8aPABhmSCUxNo/IrHgmdo4hJi6G2JR44rOTCQwN+tL+o3rs2Qoh/MlXLf/ngH8CLxw27w5gqdb6fqXUHd77twOnAoO9t0nAo96fA9rbB9/m3nX3MiNtJmNOHYXlRwmEzz8NR20n9qYuWuoaaWpoormpGWVzM4hE3K123m3/lFpacB/Wek92RzPUPgqHOZaESAc5EWFEx8YQkxRLTHoCYVHhnx80PpWh/nrKQgg/8kn4a61XKaUyvjL7TGCWd/p5YAWe8D8TeEFrrYH1SqkIpVSi1rrKF7X0Ntql0TYn7m4X7m4nutuJu+uL6c7WTvZV7KWtvIB7uJ7Aqgg2292MKY2hY+M6Fpt3UmKog8MG+ESpUAbFJ2JODiG9PZ1UC0RERWAx2rBu+Sv7nUG8P34G9545steflE0I4R892ecff1igVwPx3ulkoOyw9cq9874U/kqpa4FrAdLS0o6qAEeHnaL/HDrCEv21SXXYfYX+YhWtwe1GacCtUVqD1hgwoDRopwuX04HDZcfucuBwOXC4HSS7o1BOKHfXUWVoohsHNuXAhgOncnOmfQIAy827KTDWeB+sA7qrCTEFMW36FAxBZoY0OEnR3URERxIVH01EbBTh4eGYTJ4/3akMAyB/2UukrLqFbm2hcdqD/HXemKP6nQkhBobjcsBXa62VOqxf4vtt8wTwBEBeXt4P2vYz9jYHrfsL2Gkq+Xye8v47wZlNhA6mwtDIXmM5Gu3Ne8/PaY6hhBBAoaGGXaZSXLhw4caFGzeaU7rHE6AD2Wk6wF5r+dcee0bzFAxYOWAuoiqoGaMbDC43Brcb5XKxpMuG06hobKom0F2L0eXGbLJiNpoxm5xsrOrEbDHSVt+IvasOW2AtzcGBBAQHExQeStqosVgDTbid7Rx897dMqH6ZvYYcLBe/xKxBcgVZIcS368nwr/msO0cplQjUeudXAIefjSzFO8/nAmMCcc4MoXFzC1999zAsiCcyM4eCDaup39UMgELh6QpXGM/LIDwlBde6FTj3ODAoAxZlxGAwoZSBsLlDCQuPJn67ne5CG0ZlwKgUBs9nAlIWjMBotGAqcBNfdhC304XL6fziNtRF4a61ZNU10mmxoUJDcLuduOydOHUL1QUtOGwuWmvW47Lt+3LxKpiAiJ8AYG9/F7ejlFXMwmAKwnTfXwkITSAr72ICQiw0V29BKRshkRGERkUQHhdJZGIskQkJPfErF0L0EcrT9e6DHXn6/D84bLTPX4GGww74Rmmtb1NKLQBuwDPaZxLwiNZ64rftOy8vT2/evNkndfYGHxV/xO0rb+OeRaGMcieR/vgTmCIjv3F9e5eN9uZ2Ops76Gxtx9Zpo6G5g6c2dRPcdIgxIW2EmsHe2Y69uwMIICjyVLraHHQ2voh21Xxpf8qYRFjipQSFWWmtegOwExgWRUhkNGGxscRnZpE1LpegcAsGueCJEH2WUmqL1jrvSMt8NdTzVTwHd2OUUuXAb4H7gTeUUlcBJcBnXyn9EE/wH8Iz1PNKX9TQVywuWcwdK28nN24sc579B4HahCEo6Fu3sQRaiQq0EpUYjcvlZt3L9zK14BG2BF/NKT+7i1Ep4UfcTmuNvesEmmubaaltorWuibaGZhx2sAbH09lqp602hO72WjpbDlFfsg1wYzDnsPrNDpRBYWt5CqPZSkBINCERMYTFxZM0ZDgZo0YQFhOASQ4oC9En+azl35P6S8t/aelSHnzzZm5YZmHKE28Snpzxg7avq62h+JkrmdD9KdtDppN19XOERcT4pDbt1nS22miorKejsQuXO4i2hk4OrPsPnS0N2DobcTmaQdsxWsdjDpqJ1g7srU9iDogiMDSa0Jh4IhMSSBs5itQRgwgKs8gpmIXwox5v+YvvtqJsBX96/xb+8IYmwmwlUP+wX/3mdctJ+Pgn5Op6tg2/ldzz70L58Bu3yqAIjgggOCLlS/OnnH3n59Mul5umqgbaGrpx2Mw0VDSQv3YsnS11tDdW0FKzl/I9LvatnYUpYBxGYyu2tncIDI0jLDaRqORk4rPSyRwzlLCYCJ/VLoT44ST8j4NV5av49Yc38Yc3INxlIf2Fp7GkpHz3hoDd6ebvn+Szc80mHrRqqs9+i7FjTuzhio/MaDQQkxJLzOelJzDl7BGfL3fYHNQUVdHZpuluN1JdUEzR1gQ6W2tpqz9AxT4XuwBz8GmERI8gOLwNe/tOYtIzSR4ymIxROYTGBMunBSGOA+n26WFrKtbwy4/+j3tfN5BS7ST96acImjDhe21bVl3PS688w+O1I7hkUhq/npdNQOC3Hx/orZw2J5UFFVTmF6EMsXS0GCjft5G6ondBO7xrGTCYoknIuZD4rDTCY4zEpEYQlx5BYKhcl1eIH0q6ffxkbeVafr7s54wOSCcj0E3CAzd+r+DXWrN85RLSlt/EraqKyWd9zImTRx2HinuOyWoibXg6acMPPy31MNyuS6kqKKVkVz7VBw/RWFmCJTCMoh31tNUtx9W9BWVKwBKUQmRCFok5Q0nMTiImJYSoxGCMZjnZnBBHQ1r+PWRF2QpuWX4zmWEZPDXvGcJNISjTd7/XNra2s+7ZOzml8WXajWE4zniMuNx5PV9wL6O15tCmbez/9FOqCw/QVl+KdrtAWbCGX49SCu0qJjQ6mMTsQcRnxRGTGkJMSghWuTKYEIC0/I+7Dws/5O7Vv+L/1oQyyxJP+ILg7xX8y/ZVEfv66SzgIPkJCxj0o39iDB6Y59VUSjF44jgGT/RcYtJpt1NbXEBrQwMJWeOoK2tjyZOvU19UQX0R7DZEokzxGE0ZRCaPIzYtlLj0MGLTQolNCyUgWN4QhDichL+PvXngTR5Ydi/3fhJC9t56Ai9Lhe8I/o7OLu5bdIBXN5VzU+RcwmfcyZAp5x6nivsGk8VCUs6wz68OFhEfRHLOX6gtPER14SEq8vOpKTxEaEwIsWlh1Ja0sG/1CxiM8RjMaYTHpxCfHkZsuudNIS49FEuAvPzFwCXdPj703O7neHPR3/j1e2bCm53E/+pOIi+66FtHr+zZvILA//0fD9jPInnaxdw8JwerSb44dbRcTgdGk5mutlZeufuXNFd7zhxisoRhtGagGYHBlAgKohKDic8IIz7Tc4tKDMYg1xgW/ci3dftI+PuA1pp/bf8XT217jMefsRCpQkh5+GGCxo39xm26O1rZ+vLdTKx4kSZDBI0nPcCQaWcfx6oHhta6Wop3bqNk13ZKd21nxqU/ISxuFIXb8incvBy7PQGXMxFlCMBkMRCX7nkjSMgKJyErnKAwGWUk+i4J/x7k1m7+tv7PvJT/MmcNPofbgs/BmpSEOS7uG7fZvexV4lbfRZxuYFPEfIZd+U9CwqOPY9UDk9vtQrs1RpOJ/HVr+OjfD+K020ApwmNTCQjPwmSdQFO1G7fL8/8iPDaQhGzPG0FidjiRicFyviPRZ0j49xCX28X9H93BkIc+wDRuDGf86RUM6pu7DWpburj3f/vQu9/hxoD/YT/5j4w6YeCN5OktnA4H1YfyKd29k7K9O6kpOMRPH38Bg8nKmlf/Q9m+fUAsne3hOOzRKGXFEmAkPiucpEHhJA6KID4jTM5vJHotGe3TA9rsbTz0wvWc+PgmIrqNpEy+6BuD39VcTsnrv+T9ygg+cZ3B9SdeRsas32A1ywgUfzKZzaQMG0nKsJHAxZ8fLwCwBrnpaimhreGLRkd4fAaDJv4fVYeaWff2RpQhAIMplPiMcJIGh5OYHUFCdriMLBJ9grT8j0J+Yz5v/PlqzvygHnd0OEMfe4aA4cO/vqK9k5qP/kLE1n+DdvNe+MVM+NGfyIwJPv5Fi6PS2dJMTVEBNYWHcDkdTF14KQDP3XoDDWXFGM0BmCxxuJyRKFMqRmsO0ckhJOdEkJwTSdLgCHkzEH4j3T4+9GHhh/z7f7/hT491oMaPZtgjj2OMiPjaeq27P0K/dyPhjhoWqynok+5hztSJct6afqLywD5qi4uoLy2mvqyYutISErJHkzH2YsrzmyjZ/iKoKAzmVGLTckgZEvPFm0GIvBmI40O6fXzA4XLw8Oo/8XzJm4wbNI7oZ35MYt40lPHL/b0tHV089Wkpm9Yc5FcEs37wP7ng3AsJl2+d9itJOcNIyhn2+X2tNU6bDXNAAKNmxfKf32tqizfi6t5A1V4TNQcS2WYdj9GcRUxqCMlDIkkZ4nkzkO8bCH+Qlv/3UNtZy78ev4b5Lxwg/+oTuei6hzEbvhzmXdX5VP3ndrbVG7il+yoWjE7kFycNZlB8qJ+qFv5m6+ygfN8eyvbspHTPToZMOQ2jZTAFW/ZQuf8dlCEeozmRmPTBZOYOInVoNAlZ4XK+IuEz0vI/BpsrN7LkD9ezcGk7zowELlxw25eC39ZWT8GbvyWn9FXitQkdeQkfnD+NkclHvrqWGDisQcFkj59I9vgvX6U0aZCNNa/FU33oIPaOnVTu/ZjKfRYsIQuxBCYQn2EheWgMmWOSiE6WU1yLniEt/2/Qam/lxTX/IPShV5hwwI2aM4OcPz/4+SUXW7sdrFv6HlM2/Zxg3cHyoHnEnH4PucOHHNc6Rd+l3W4aKyuoOpRPxf79ZI4/k5rCLvauepv2+k9RxjgsQWkkZA8jZ1Iu2ePSCY6w+rts0YfIAd8foKuhlrfz3+LR0pfJ2tPM7W+5ibz1FyReeTV0NVG66mU+qArjH4VxxDmreCD0ZTj5HsZPmCotNOET1QUH2ffpWop37KSpsgDtdgIWrBE/Izo5lJhkG1ljM8kYlSxdROJbSfh/B1dbG61LllDw1vMEbM3nvcmK0oumc9OY/yOrxYy9fQ8Na18guW41Zpy8pOexN/duLpqQ9o0XTxfCFzxfRDtA5cEyTAHDKNvbSMHGR9CuegzmBCITh5I5diyjZuURmRgqDRDxJb0y/JVS84CHASPwlNb6/m9atyfDv+K222hZtAjlcFIbDgfGxjD6vMtJm3AJm4qbGLPoLNK786nVEawLOpGA8RcxbdpsggNk9I7wj5Kdu9i9ch1le7bT0VQGaAyWocSkn0PaiGiik10MmZSD2SqH9Aa6Xhf+SikjcACYA5QDm4CLtNZ7j7S+r8Lf2dRE+9KlNO/aTtVPFnCg6QDmh56jsquG/JGhTI+K5IyafCyuTkZ3P4ETE2cFbGX8oGQmzD6LoUkD89z6ovfqbm9n/7pNtNYr2hojKd1bTGf9kyhDGGFxQ8gYM47cOVOISY3xd6nCD3pj+E8Bfqe1nuu9fyeA1vpPR1r/aMO/02FjzdpFdH3yFqFbDxBf3IpBQ3UE3H6lka4ARbjbyk+bqlnY2kaTjmCrYST1MZNwjVzI+OxEhiWGYpLT/Io+or25hU3vfUzhti20VB9EazugiEy9gCGTJ5E2PILEnChMctrwAaE3DvVMBsoOu18OTPL1gxQ31fLf9+/iZ/9zUxENqye66chwEBRqILL+HFxqAsPMLVjDSlk8dibDRoxlXmyI9JuKPiskIpwTL1/IiZcvxOV0cGD9DvauXocypbNzRTmbP3gfl30LUUkjGDxpCrmnTCE4PNDfZQs/6LWdgkqpa4FrAdLS0o5qH4Oikhg8+2dUjWsnZdR0hkQmExQeS2hQANeZpDUv+jejycywaXkMm+Zp+Nm7nWz5n2b3yhoayjfRULaO9W9ZCYkawoQzriUzN5bIBDnv1EDRr7t9hBBHZu/qYsfStez/dC2tdU1gOg0Ao2EDsalxjD5pOtl5g+XaBX1cb+zzN+E54HsSUIHngO/FWus9R1pfwl+IntXW2E3R9lpWvnQv9o5qAAzmaGLSRjNy1ixGzcqV6xb0Qb0u/AGUUvOBh/AM9XxGa/2Hb1pXwl+I46emuIxtH62keMcmOhqLMAWcQED4ZJJzQgiJqCVvwQxCIkP8Xab4Hnpl+P8QEv5C+EdnaxtVB5upONDJ/rVraKt9BzATEj2E7PFTyFswk4iECH+XKb6BhL8Q4pg5HQ52L9/I7hUrqSvegdvVARhJHvF/5EweTFZuDBFxcsC4N5HwF0L4lNvtYv+nW9mzagNuJlJf1o6jYwkmSydpI/MYv2A2yTlJ/i5zwJPwF0L0qNaGLpY+8wKlO9fgtDcBCktgCum5MzjhvNOITpbvz/iDhL8Q4rjQWlO6+wBbFy2jfO9mXO4kzEEnEhZjJTBoD2PnziJr3GB5IzhOeuM3fIUQ/ZBSivRRQ0gf5bmuRVtjJ6V7mti7egclO96nZMf7mKzxJA2ZwPj5J5OZmy1vBH4iLX8hxHFRV1rJxveXULx9Hd1tnrO7RKZexvDpeWSPjSEmNUzeCHxMun2EEL1KbXE5m/67FIdjBJUHW7F3rsFAOSnDJzDh9DmkDs/wd4n9goS/EKLX6my1s+qVdzm0cQm2jkoAzIHJZOROY9al5xEWIyeeO1oS/kKIPqHqYAkb/7uY0l0bcDpCsYScRnxmGEGhBeTNn0FcRqK/S+xTJPyFEH1OU1UrRTub2Lc2n+r9jwAQGJ7N4AkzmHT2HMJiwvxcYe8n4S+E6NOKdx5k0/sfU7F/PS5HM2Aibczl5J5yAhmjYzDLSeeOSMJfCNEvuN1u9q7eyraPF2N3TKC7zQDufYREtDLm5JMYNXs8RrlK2eck/IUQ/Y7brak82MzKF5+j+uAKwIXBFEHioImMP20Og/KGDvihoxL+Qoh+rbOljQ3vLiF/3Uo6mgpQpmQSBl9BzsR40kYEE5ce5+8S/ULCXwgxYDRU1HBwYwlVhSYq8quwtTxBQEgq2XlTmXLuXMLjovxd4nEj4S+EGJBqS2pZ/epblO1Zj8veABgIjRnC5HMuZcSsURiN/fta3hL+QogBze12c2D9brZ8+Ak1hVswBy8kOCKGxKwu4jOtjD7pBExms7/L9DkJfyGE8HI6XZTtbSJ/fRX71zyPy7YfZQwkcXAeE86YR/bYUShD//hEIOEvhBBH0NHSyYZ3V3Bg3So6mvYDToIisjn5ml+ROSYGk7lvDxuV8BdCiO9QW9LA+rcXU1XYitMxCEsguG3vMmjiRCacNpewmFh/l/iDSfgLIcT35HZrKvKb2Ll0DwfWvYDbWQVAZNJQxs47hZEzZ2AOCPBzld/Pt4X/MXVsKaXOV0rtUUq5lVJ5X1l2p1LqkFIqXyk197D587zzDiml7jiWxxdCCF8zGBSpw6JYcMN0rn/qUWZcdh8RiTNprq5k2TOP8O6DiyncVkdXWzva7fZ3uUftmFr+SqlhgBt4HLhVa73ZO3848CowEUgClgA53s0OAHOAcmATcJHWeu+3PY60/IUQ/tZY2cam/62n4mAAXa0O3I5l4C5h6AmzGL9gLlFJKf4u8Wt67DKOWut93gf46qIzgde01jagSCl1CM8bAcAhrXWhd7vXvOt+a/gLIYS/RSWFMveaObhdbkr3NrLxvVoq9jezc8m77FzyDuHxmUw4/XTGzDnF36V+Lz11Dd9kYP1h98u98wDKvjJ/0pF2oJS6FrgWIC0trQdKFEKIH85gNJAxKoaMURfS1X4Ou5YfYMfiJbTWb2fFyyupr0xh6AmJ2NoOkT46F6Opd35/4DvDXym1BEg4wqK7tNbv+b4kD631E8AT4On26anHEUKIoxUYYmHi6SOZcNoIaopa2bOmlIKtdexdvRV722uYrcEMOWEGuafMJS6zd12s/jvDX2t98lHstwJIPex+ince3zJfCCH6JKUUCVnhJGSNYsYFLg5srGTbx0bqijeze/kn7F6+iLDYFM6589dEJyd/9w6Pg57q9nkfeEUp9QCeA76DgY2AAgYrpTLxhP6FwMU9VIMQQhx3ZquREdNTGTH9Ipqqz2Dn8gL2rlxBe1M+//3nIYZPtWMNKCYozErWuAl+6xY6pvBXSp0N/AOIBf6nlNqutZ6rtd6jlHoDz4FcJ3C91trl3eYG4GPACDyjtd5zTM9ACCF6qciEYGZeNJpp54+kaEc9e9dUsOmDIuxtr+N2VmAJDGH4jFnkTJ5K8tDhGAzH7xvF8iUvIYQ4jlrru9izuoxdy9bR0bQdt6MQcDF40kzOuPmXALicDp98IpBv+AohRC/jdrkp2d3ArpVFlOzYCiqYjDGjyBhpZsXzvyMzN4/Bk04ga/xEzBbrUT2GhL8QQvRibY3d7P20kn1rKmlrrEW5t+B2FuK0tfPTJ14iKCz8qPbbY1/yEkIIcexCowKYdHoWE+ZnULyrgb1rsineXYfZ0sDq18s45eownw8TlfAXQohewmA0kJUbS1ZuLK0NXez7tArt1j3y/QAJfyGE6IXCogOZdEZWj+2/f1yuRgghxA8i4S+EEAOQhL8QQgxAEv5CCDEASfgLIcQAJOEvhBADkIS/EEIMQBL+QggxAPWJc/sopeqAkqPYNAao93E5viK1HR2p7ehIbUenr9eWrrWOPdKCPhH+R0sptfmbTmrkb1Lb0ZHajo7UdnT6c23S7SOEEAOQhL8QQgxA/T38n/B3Ad9Cajs6UtvRkdqOTr+trV/3+QshhDiy/t7yF0IIcQQS/kIIMQD1y/BXSs1TSuUrpQ4ppe7ww+M/o5SqVUrtPmxelFJqsVLqoPdnpHe+Uko94q11p1JqXA/XlqqUWq6U2quU2qOU+nlvqU8pFaCU2qiU2uGt7R7v/Eyl1AZvDa8rpSze+Vbv/UPe5Rk9VdthNRqVUtuUUh/0ptqUUsVKqV1Kqe1Kqc3eeX7/m3ofL0Ip9R+l1H6l1D6l1JTeUJtSaoj39/XZrVUpdVNvqM37eL/w/j/YrZR61fv/w3evN611v7oBRqAAyAIswA5g+HGuYQYwDth92Ly/AHd4p+8A/uydng8sAhQwGdjQw7UlAuO806HAAWB4b6jP+xgh3mkzsMH7mG8AF3rnPwZc553+GfCYd/pC4PXj8Le9GXgF+MB7v1fUBhQDMV+Z5/e/qffxngeu9k5bgIjeUtthNRqBaiC9N9QGJANFQOBhr7MrfPl66/Ff6vG+AVOAjw+7fydwpx/qyODL4Z8PJHqnE4F87/TjwEVHWu841fkeMKe31QcEAVuBSXi+xWj66t8X+BiY4p02eddTPVhTCrAUmA184A2B3lJbMV8Pf7//TYFwb4ip3lbbV+o5Bfi0t9SGJ/zLgCjv6+cDYK4vX2/9sdvns1/aZ8q98/wtXmtd5Z2uBuK9036r1/vRcCyeFnavqM/brbIdqAUW4/kU16y1dh7h8T+vzbu8BYjuqdqAh4DbALf3fnQvqk0DnyiltiilrvXO6w1/00ygDnjW2132lFIquJfUdrgLgVe9036vTWtdAfwNKAWq8Lx+tuDD11t/DP9eT3venv06xlYpFQK8BdyktW49fJk/69Nau7TWuXha2ROBof6o46uUUqcBtVrrLf6u5RtM01qPA04FrldKzTh8oR//piY8XaCPaq3HAh14ulJ6Q20AePvNzwDe/Ooyf9XmPc5wJp43zyQgGJjny8foj+FfAaQedj/FO8/fapRSiQDen7Xe+ce9XqWUGU/wv6y1fru31QegtW4GluP5aBuhlDId4fE/r827PBxo6KGSpgJnKKWKgdfwdP083Etq+6yliNa6FngHzxtnb/iblgPlWusN3vv/wfNm0Btq+8ypwFatdY33fm+o7WSgSGtdp7V2AG/jeQ367PXWH8N/EzDYe1Tcgufj3Pt+rgk8NVzunb4cT1/7Z/N/5B1JMBloOewjp88ppRTwNLBPa/1Ab6pPKRWrlIrwTgfiORaxD8+bwHnfUNtnNZ8HLPO21HxOa32n1jpFa52B5zW1TGt9SW+oTSkVrJQK/WwaT//1bnrB31RrXQ2UKaWGeGedBOztDbUd5iK+6PL5rAZ/11YKTFZKBXn/z372e/Pd662nD6T444bnqPwBPP3Fd/nh8V/F00/nwNPyuQpP/9tS4CCwBIjyrquAf3lr3QXk9XBt0/B8jN0JbPfe5veG+oDRwDZvbbuB33jnZwEbgUN4PppbvfMDvPcPeZdnHae/7yy+GO3j99q8Nezw3vZ89prvDX9T7+PlApu9f9d3gcheVFswnhZy+GHzektt9wD7vf8XXgSsvny9yekdhBBiAOqP3T5CCCG+g4S/EEIMQBL+QggxAEn4CyHEACThL4QQA5CEvxBCDEAS/kIIMQD9P3XXUflBy64bAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -415,7 +650,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -427,7 +662,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -470,19 +705,798 @@
     "    ax.plot(temp,U_pred,'--')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Option 2: featurize on the fly without using the database"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-02-24 14:28:31,232 - modnet - INFO - Loaded DeBreuck2020Featurizer featurizer.\n",
+      "2021-02-24 14:28:31,233 - modnet - INFO - Computing features, this can take time...\n",
+      "2021-02-24 14:28:31,234 - modnet - INFO - Applying composition featurizers...\n",
+      "2021-02-24 14:28:31,241 - modnet - INFO - Applying featurizers (AtomicOrbitals(), AtomicPackingEfficiency(), BandCenter(), ElementFraction(), ElementProperty(data_source=<matminer.utils.data.MagpieData object at 0x7fa329059d90>,\n",
+      "                features=['Number', 'MendeleevNumber', 'AtomicWeight',\n",
+      "                          'MeltingT', 'Column', 'Row', 'CovalentRadius',\n",
+      "                          'Electronegativity', 'NsValence', 'NpValence',\n",
+      "                          'NdValence', 'NfValence', 'NValence', 'NsUnfilled',\n",
+      "                          'NpUnfilled', 'NdUnfilled', 'NfUnfilled', 'NUnfilled',\n",
+      "                          'GSvolume_pa', 'GSbandgap', 'GSmagmom',\n",
+      "                          'SpaceGroupNumber'],\n",
+      "                stats=['minimum', 'maximum', 'range', 'mean', 'avg_dev',\n",
+      "                       'mode']), IonProperty(), Miedema(ss_types=['min'], struct_types=['inter', 'amor', 'ss']), Stoichiometry(), TMetalFraction(), ValenceOrbital(), YangSolidSolution()) to column 'composition'.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43ba6a6745f04ed5954b894f89c1510f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='MultipleFeaturizer'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "2021-02-24 14:28:37,752 - modnet - INFO - Applying oxidation state featurizers...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9419d813844e4840beddd4da89625f09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='CompositionToOxidComposition'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "2021-02-24 14:28:41,757 - modnet - INFO - Applying featurizers (ElectronegativityDiff(stats=['minimum', 'maximum', 'range', 'mean', 'std_dev']), OxidationStates(stats=['minimum', 'maximum', 'range', 'std_dev'])) to column 'composition_oxid'.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc24c15e0c8c47629f2138ba35175f8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='MultipleFeaturizer'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "2021-02-24 14:28:45,113 - modnet - INFO - Applying structure featurizers...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ppdebreuck/anaconda3/envs/modnet-develop/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
+      "  warnings.warn('From version 0.24, get_params will raise an '\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-02-24 14:28:45,340 - modnet - INFO - Applying featurizers (DensityFeatures(), GlobalSymmetryFeatures(), RadialDistributionFunction(), CoulombMatrix(), SineCoulombMatrix(), EwaldEnergy(), BondFractions(), StructuralHeterogeneity(), MaximumPackingEfficiency(), ChemicalOrdering(), XRDPowderPattern(pattern_length=128)) to column 'structure'.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa3989bc206b45a78b62de9a5957419b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='MultipleFeaturizer'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "2021-02-24 14:28:57,526 - modnet - INFO - Applying site featurizers...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d5b9cb746b54e3c83898e6d5cb59ea5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8fbb30c86aa411ca4f7626b49f16d43",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47630729b43e4e6e8ae928d78e50d0b0",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94054f40643c47dca91f231960548e04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a7153e10f3b418b8b54e13ba02ba6c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ef357779e9a44a192f592022c1ddfbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a059d6e5241427d8c296e5b0a15f714",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34d939f975734ce9b13fb2496558b317",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7333b8348d464c0a9324f2180c076f89",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39ef6c60c79d4262b9344b1a0f688497",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0fdd5ab6aba14112a1771375e010a178",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "875df0c31cb445eab45f28e2fdba090e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='SiteStatsFingerprint'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "2021-02-24 14:30:03,989 - modnet - INFO - Data has successfully been featurized!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Option 2: featurize on the fly, if you prefer not using the database\n",
+    "md2 = MODData(df['structure'],\n",
+    "              structure_ids = df.index\n",
+    "             )\n",
+    "md2.featurize(fast=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BondFractions|Al - N bond frac.\n",
+      "BondFractions|B - Ca bond frac.\n",
+      "BondFractions|Be - Li bond frac.\n",
+      "BondFractions|Al - S bond frac.\n",
+      "BondFractions|B - Li bond frac.\n",
+      "BondFractions|Al - P bond frac.\n",
+      "BondFractions|Al - C bond frac.\n",
+      "BondFractions|Be - Ca bond frac.\n",
+      "BondFractions|Ca - P bond frac.\n",
+      "BondFractions|B - K bond frac.\n",
+      "BondFractions|B - P bond frac.\n",
+      "BondFractions|Al - Si bond frac.\n",
+      "BondFractions|B - Cr bond frac.\n",
+      "BondFractions|B - S bond frac.\n",
+      "BondFractions|C - Sc bond frac.\n",
+      "BondFractions|Cl - Ti bond frac.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# missing features:\n",
+    "missing_feats = []\n",
+    "for f in model.optimal_descriptors[:model.n_feat]:\n",
+    "    if f not in md2.df_featurized.columns:\n",
+    "        print(f)\n",
+    "        missing_feats.append(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are bondfractions of non-appearing bonds in our test set. This is normal. We can safely set them to zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "md2.df_featurized[missing_feats] = 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_predictions = model.predict(md) # you can either use md or md2 here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>S_5_atom</th>\n",
+       "      <th>S_10_atom</th>\n",
+       "      <th>S_15_atom</th>\n",
+       "      <th>S_20_atom</th>\n",
+       "      <th>S_25_atom</th>\n",
+       "      <th>S_30_atom</th>\n",
+       "      <th>S_35_atom</th>\n",
+       "      <th>S_40_atom</th>\n",
+       "      <th>S_45_atom</th>\n",
+       "      <th>S_50_atom</th>\n",
+       "      <th>...</th>\n",
+       "      <th>H_760_atom</th>\n",
+       "      <th>H_765_atom</th>\n",
+       "      <th>H_770_atom</th>\n",
+       "      <th>H_775_atom</th>\n",
+       "      <th>H_780_atom</th>\n",
+       "      <th>H_785_atom</th>\n",
+       "      <th>H_790_atom</th>\n",
+       "      <th>H_795_atom</th>\n",
+       "      <th>H_800_atom</th>\n",
+       "      <th>eform</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mp-14363</th>\n",
+       "      <td>0.014347</td>\n",
+       "      <td>0.018734</td>\n",
+       "      <td>0.238954</td>\n",
+       "      <td>0.606634</td>\n",
+       "      <td>1.057049</td>\n",
+       "      <td>1.684448</td>\n",
+       "      <td>2.379784</td>\n",
+       "      <td>3.057159</td>\n",
+       "      <td>3.625864</td>\n",
+       "      <td>4.372535</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-17274.916016</td>\n",
+       "      <td>-17516.820312</td>\n",
+       "      <td>-17742.035156</td>\n",
+       "      <td>-17984.978516</td>\n",
+       "      <td>-18219.955078</td>\n",
+       "      <td>-18462.693359</td>\n",
+       "      <td>-18669.978516</td>\n",
+       "      <td>-18922.746094</td>\n",
+       "      <td>-19187.947266</td>\n",
+       "      <td>-1.895996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mp-988</th>\n",
+       "      <td>-0.010973</td>\n",
+       "      <td>-0.001627</td>\n",
+       "      <td>-0.016520</td>\n",
+       "      <td>0.028203</td>\n",
+       "      <td>0.086789</td>\n",
+       "      <td>0.061554</td>\n",
+       "      <td>0.071316</td>\n",
+       "      <td>0.221528</td>\n",
+       "      <td>0.156996</td>\n",
+       "      <td>0.339230</td>\n",
+       "      <td>...</td>\n",
+       "      <td>714.511047</td>\n",
+       "      <td>561.200562</td>\n",
+       "      <td>504.741852</td>\n",
+       "      <td>382.601746</td>\n",
+       "      <td>291.089142</td>\n",
+       "      <td>157.871490</td>\n",
+       "      <td>112.379631</td>\n",
+       "      <td>-7.474174</td>\n",
+       "      <td>-194.458313</td>\n",
+       "      <td>-0.710409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mp-38487</th>\n",
+       "      <td>-0.008141</td>\n",
+       "      <td>0.007511</td>\n",
+       "      <td>0.027280</td>\n",
+       "      <td>0.079071</td>\n",
+       "      <td>0.160426</td>\n",
+       "      <td>0.249019</td>\n",
+       "      <td>0.380474</td>\n",
+       "      <td>0.536368</td>\n",
+       "      <td>0.630777</td>\n",
+       "      <td>0.908211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-8083.064453</td>\n",
+       "      <td>-8281.423828</td>\n",
+       "      <td>-8404.014648</td>\n",
+       "      <td>-8546.526367</td>\n",
+       "      <td>-8694.868164</td>\n",
+       "      <td>-8875.536133</td>\n",
+       "      <td>-9001.016602</td>\n",
+       "      <td>-9178.028320</td>\n",
+       "      <td>-9363.007812</td>\n",
+       "      <td>-2.205507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mp-559200</th>\n",
+       "      <td>-0.010615</td>\n",
+       "      <td>0.036975</td>\n",
+       "      <td>0.161285</td>\n",
+       "      <td>0.393592</td>\n",
+       "      <td>0.742751</td>\n",
+       "      <td>1.148751</td>\n",
+       "      <td>1.614309</td>\n",
+       "      <td>2.131572</td>\n",
+       "      <td>2.705519</td>\n",
+       "      <td>3.401336</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-21751.392578</td>\n",
+       "      <td>-22014.144531</td>\n",
+       "      <td>-22267.328125</td>\n",
+       "      <td>-22514.515625</td>\n",
+       "      <td>-22766.244141</td>\n",
+       "      <td>-23032.716797</td>\n",
+       "      <td>-23287.316406</td>\n",
+       "      <td>-23562.757812</td>\n",
+       "      <td>-23815.939453</td>\n",
+       "      <td>-0.687132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mp-4661</th>\n",
+       "      <td>0.002966</td>\n",
+       "      <td>0.024075</td>\n",
+       "      <td>0.082043</td>\n",
+       "      <td>0.099225</td>\n",
+       "      <td>0.067827</td>\n",
+       "      <td>0.083071</td>\n",
+       "      <td>0.112224</td>\n",
+       "      <td>0.205507</td>\n",
+       "      <td>0.260845</td>\n",
+       "      <td>0.519051</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-10469.830078</td>\n",
+       "      <td>-10686.641602</td>\n",
+       "      <td>-10830.547852</td>\n",
+       "      <td>-10990.991211</td>\n",
+       "      <td>-11161.797852</td>\n",
+       "      <td>-11356.781250</td>\n",
+       "      <td>-11511.146484</td>\n",
+       "      <td>-11712.181641</td>\n",
+       "      <td>-11910.938477</td>\n",
+       "      <td>-3.540361</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 641 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           S_5_atom  S_10_atom  S_15_atom  S_20_atom  S_25_atom  S_30_atom  \\\n",
+       "mp-14363   0.014347   0.018734   0.238954   0.606634   1.057049   1.684448   \n",
+       "mp-988    -0.010973  -0.001627  -0.016520   0.028203   0.086789   0.061554   \n",
+       "mp-38487  -0.008141   0.007511   0.027280   0.079071   0.160426   0.249019   \n",
+       "mp-559200 -0.010615   0.036975   0.161285   0.393592   0.742751   1.148751   \n",
+       "mp-4661    0.002966   0.024075   0.082043   0.099225   0.067827   0.083071   \n",
+       "\n",
+       "           S_35_atom  S_40_atom  S_45_atom  S_50_atom  ...    H_760_atom  \\\n",
+       "mp-14363    2.379784   3.057159   3.625864   4.372535  ... -17274.916016   \n",
+       "mp-988      0.071316   0.221528   0.156996   0.339230  ...    714.511047   \n",
+       "mp-38487    0.380474   0.536368   0.630777   0.908211  ...  -8083.064453   \n",
+       "mp-559200   1.614309   2.131572   2.705519   3.401336  ... -21751.392578   \n",
+       "mp-4661     0.112224   0.205507   0.260845   0.519051  ... -10469.830078   \n",
+       "\n",
+       "             H_765_atom    H_770_atom    H_775_atom    H_780_atom  \\\n",
+       "mp-14363  -17516.820312 -17742.035156 -17984.978516 -18219.955078   \n",
+       "mp-988       561.200562    504.741852    382.601746    291.089142   \n",
+       "mp-38487   -8281.423828  -8404.014648  -8546.526367  -8694.868164   \n",
+       "mp-559200 -22014.144531 -22267.328125 -22514.515625 -22766.244141   \n",
+       "mp-4661   -10686.641602 -10830.547852 -10990.991211 -11161.797852   \n",
+       "\n",
+       "             H_785_atom    H_790_atom    H_795_atom    H_800_atom     eform  \n",
+       "mp-14363  -18462.693359 -18669.978516 -18922.746094 -19187.947266 -1.895996  \n",
+       "mp-988       157.871490    112.379631     -7.474174   -194.458313 -0.710409  \n",
+       "mp-38487   -8875.536133  -9001.016602  -9178.028320  -9363.007812 -2.205507  \n",
+       "mp-559200 -23032.716797 -23287.316406 -23562.757812 -23815.939453 -0.687132  \n",
+       "mp-4661   -11356.781250 -11511.146484 -11712.181641 -11910.938477 -3.540361  \n",
+       "\n",
+       "[5 rows x 641 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_predictions.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot of the results, compared with DFPT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Z7QoxqngcOHb+k+6Nf+Nb6hY21ruYabyO/An5LJ87i4cmJmMxSTu+OHWkEVGIYaRb9tH65h+JOvgPwv39NPrTiImu5/azF3P+jBUkRVlDXaIYpyT8hRgGHf0uXnvrHS7ddAGx2sSrzKUh/zLmnH4e90n3TDECSPgLMUT8zXtpfON+DrX0cVPbxXh8mpakb5E97wJWlJVKbx0xosinUYiT4eqnf/vf6d/wEKm95SRpE9vUIq6Zl8Nlc7IpSjkn1BUKcVwS/kJ8VlqjtWZzdRd9z9/Ois7HafJnsDbmRpIXXcuqWZNYYzKGukohPpaEvxCflteFY8tfcLxzP3f4r+TvnYUUhy2itngJi5edw7Up0aGuUIhPTcJfiE/iddH85v2EbfodsZ5WKv252OKM/PLiqayemo7NInv5YvSR8BfiI3h8fl7e08zE588j31PBNl3MjgnfY97yC/lxZmyoyxPipEj4C/EBbV097H7xfn5QPY2GPg/XRK9h7vQCFq68kFkR0i9fjA0S/kIAaE1F+SYOr3+C6W3PsFx1sSvpJ0y78DJOLz4bo0H65YuxRcJfjGs+v2bd9j2UvHQJhb4m8rWiOnoWjctv5dvTzwh1eUIMGwl/Mb74vFD1Ju69z7K/y8BXWtdQ32Xn3oiJRE28gekrr2BCQsYnP48Qo5yEvxgffB7Y9QSedXdh7qnGo8Mo9y0kLTOM/3fOJFaWnCNNO2JckfAX40LbM7eTVP5HDvhz+aPvm4SVnsU1iydylfTaEeOUhL8YmzwO9LZH2KaLuHtPOLVVk5hiuY3suWv4f4smyEVSxLgn4S/GFrcd35YH8az/DWGudjZ7z+Nw+LVcv2oxn5ubTXSYOdQVCjEiSPiLMcO96UG8r/2UcHcHG32lPB39beYvW8Nb09LlQilCfICEvxjdPE56PAb+tqkW0/oNlHpTWZv8PZatXMNdxUkybr4QH0HCX4xOXhf9Gx5Er7+bH7iv5VnXTJYV3cDMZcX8JDc+1NUJMeJJ+IvRxeui592H4K27ifG0ssVfTGZ2Hs+ftYjJGXLhcyE+LQl/MWo0djtw/WkVeQM72e4vZEvebSw/+1K+kxIV6tKEGHUk/MXI5nHSteERftM2i8e2t7JUrWRe0bWcsfoyboqPCHV1QoxaEv5iZPI46H77AQzv/IY4bzs93q9xadllfHnpUjJibaGuTohRT8JfjCx+H93r7sX4zq+J9XawyT+JPYU/4DurLyEjLjzU1QkxZkj4ixGjrc/FPa9XsGbbIzhJYW/Rjzhr9SXMlT19IYachL8ILa0Z2P0cPa/cySU936DZF4We/ntuPGM68yX0hRg2Ev4iZJwV6+h+7nZS+8pp9qexZgJccu4S8hLlQK4Qw+2kw18plQX8BUgBNHC/1vq3Sql44EkgF6gGLtVad6nAKZe/Bc4G7MB1WuvtJ1uHGD1cLidt919IZsc7aB3Pn5P+iznnf5XvZsrJWUKcKkOx5+8F/ktrvV0pFQVsU0q9AlwHvKa1vkMpdRtwG3ArcBZQGLzNBf4Q/CvGOH9fG/8+7OaXaw9yU58NFf8FSs+/hesmpIW6NCHGnZMOf611E9AUvN+nlNoPZABrgNODiz0CvEkg/NcAf9Faa2CjUipWKZUWfB4xFvU20fzvH5NQ8RT3uH5KTFopuRfex2mFSaGuTIhxa0jb/JVSucAMYBOQMijQmwk0C0Hgi6Fu0Gr1wWnvC3+l1I3AjQDZ2dlDWaY4VRxddLz8SyJ3PkC838uzpjP5xpr5nDVnKga5apYQITVk4a+UigT+CXxTa907eDRFrbVWSunP8nxa6/uB+wHKyso+07oi9Jo7erD9oYw4TwcvqoX0LfguFyxbRJjZGOrShBAMUfgrpcwEgv9RrfXTwcktR5tzlFJpQGtwegOQNWj1zOA0Mdr5vLj2vcA9TRO5/60qztOXUjB1HpeccxZxEZZQVyfEiKW1xqd9eP3e92468NeojCSFD30T6VD09lHAg8B+rfXdg2Y9B1wL3BH8++yg6V9VSj1B4EBvj7T3j3Jao/c9S/+LPyRqoJoNrh+wYspSvrbq+2TFy1m5IrR8fh9uvxu3L3gbdN/j97xvmsfvwePz4PG58bgceJ0OvC4HHnw4Ikx4vC5idlaj7A6U3YlyuDA4XDTnRlM7MQ6fy8WU147gUxovPnzajw8/R7IsHMkyY3S4mbehGz9+/H4/fu1D+/3szNEcTlfEDGhWb/Zj8IMxeNv6uen87dzHhvx9GYo9/4XA1UC5UmpncNp/Ewj9vyulbgBqgEuD814k0M2zkkBXz88PQQ0iVKrWYX/x+4S376LJn8FvYr7Pd6+9jtl5CaGuTIwwWmu8fi8OnwOX14XT68Thc+D0OgM3X+Cvu78XT38v3oF+PI4BfPYBXPhoy4nG5XOR8VYFttZeTANOjHY3ZoebjjgTz5+TiMvn4rpHGklq92Ly+jH6NCYv7MlV/PqCQJPjnQ95iRkAvwKtwG+ArQWKR1YG5j/wGy/RjvfX/soMxZ9WGTFoeOKX3g+9tnWnxVKdnkqky8Cif1d/aP7WcwuwTZtETI+H81/5z4fmz7h6IU3Ty4hq6mX6fX9FGwxoowGMBmZMGp6IVIFONyNbWVmZ3rp1a6jLEB/Q0tmN7d4Z9HkUDxgvp+SsG7loVo4czB3ltNY4fU4GPAM4PA4GvAMMeAawe+zYvXbszn6cg24ulx23y057jMLhcxBe2054Sy/K7sQQ3DP2+Tz8cx74tI81G/yU1mhsbo3NBTY39NngtusD+6L/73EfU6vfn0t1SYoffzkOq9HKLQ90klfrwhlmwGUz4bKZaMmLZePV0zAbzMx/ch/hfR6wmFBmC8pixpGbSveZZVgMFjL++jrmfhdGFAY/GFAwqQDDJasxG8wYHnoKIwpjmA2T1YYxLAxb0USi58zDaDDi2L0bg82GISIicAsPB6MRZTCgtUa7XKA1+P3o4F9ltWKwWtF+P9rphKPHRJUCgwFlMqEMQ3+pUaXUNq112XHnSfiLz8TRjWfDH3hAn8/v19VQ4KvmtAXz+dLyEqLk4ugh5/F56PP00efuo9/dT6+7l35PP33uwLQBzwD9nn4G7L34ujqhqwfV3Yuxu5+NxQZ6cTBpXx9z9/mIcEKkUxPhhAgXfOVmIx6z4ppXfaze8uHcuOWnedjM4Vz4zxZmbGp/f11hZt546AZsJhsFj20gZl89hNtQETZURAQqKRHDl67CarRiens7pq4+LBFRWCKiMIdHYY6NxTZtGgB+ux1ltaKM0nngk3xc+MvwDuLT0Rr2/BP3C7didHay3m3ktEnL+O+zTyMnQYZjGEpaa/o8ffQ4e+hx99Dt7KK3rZFu5aAHB67WZqwHa3E5BnC5B3A77XhcDjZM8NAc7ia3WXPaXj9WD1g9EOYBqxv+eqaBljjF6p0mrnnJ+aHthv3sTHRGChNrqsjp2IOOCofkcFRUFIaYaB5acS0RMYlYsg5gmF+FxRqO2WoL7F2bTDx/wfkogwH39Fp8fX0Yj+4ZR0aiwsKYenRvd+qNH/8GnFf8sbMN4XIcaSjInr/4ZJ1VOJ/5FmG1b7LLP4F7Ir7KVRecx+IiOUnr0/D5fXS5uuhwdNDh7KDL2UWXs4ue3lbs7S04u9rxdHVSHe2ixjaAramL89/2ktALCX2ahD6weOHOiw1sKzSw4IiVbz4x8KHtbLztLNwzisnaUkfO75+DMCvYwjDYwjGGh5Pwkx8SO3EK7r376F+3HlNCPMaEBEwJCRjj47FkZqLM8uttLJFmH3HC3B4fXb9fTETPYX6tLyN+yc18YUkhVtP4/smttWbAM0Cro5V2e/uxv22ONjoG2vA0NKBb2jC1d2PrsJPQ62dbvmJ7oYGULs2vHvBh+cBxw7cvL6HpzGlktPmZ9cuX8CfFoZKTMKWmEpaeQezS5cTlT0INOPDU16PM5kBbsdkMZjOm2FiURbrUivdIs4/47Go2sMmewu0v1aHarmFaQTbfuGDJuOi66dd+Op2dtAy00Gxvft/fFnsLrQMtxB1qIarLRXwfJPYG9s735CrWzQknQ8Xz85/Uvu85vVE2yuaswrrqImK9VsyOZ7AlJGGKjcMYG4sxJpbP50/AlJgYWOG8H310gVFRGCdNGr43QIwLEv7i/ToO41r7Q6yH/s0W7xpc0dfxo2vOZ/mklE9ed5Tw+D20DLTQ2N9I40AjTf1NNPbW09leR5Vuo9nezKoNblK6NDF2iLZrptkVNXkRtFxVyuTEKVz1vw2YHX4AdEQ4hpQkVpacxy+vvBmlFD2Rz2JKTsaUmoo5NRWD7QPXJvje5BC8ciHeI80+ImCgA73uTvSWB3FqI3/ynYda8FVuXDFlVA7J0O/up66v7n23+r566vrqaLY3M63CS0GTJr0DMjs0aZ3QnB3Ja7ctJS0ijSXfewZr9wAqPg5LQiJhSamEz5pF/NVXAWDftg1jTAym1DSMkXLAWwwPr8MDDh+m+LATWl+afcQn6n/uFsIPPsMT3tN5PfUGbrt0CQXJUaEu62N5/B7q+uqo7qmmurf62N+a3hrSDrRT1BBskumFRX0GzEYzL/7PGWRGZTLv5deI2H4IQ3oatkn5hE0oILe0lDMXnwOA/s83PrbfdfisWafqZYpxQPs1PXXt1B+sobmhidaONtoHuhjwO7g+ZTUpX54+5NuU8B+v/H7Y8w+8KdN48ICJv+87nQjDIi5dvYL752SPqBO1XD4X1T3VVHRXcLj7MJVdlTS1VGKsaiCjzUdWmyazHc7sVvzpB7NYmrWUJW8fIHX9LoiNxpyWjnVCBpaMTJYsvg0A728+hyEqCoPVetxtDscJN0IAODr7aTxYS3NNIy2trZTpAgxtHrZRyQ7TEQAiVBiJtjgmxOcSPn94rnch4T8edVbB0zdC/RaeCzufn3dfysqSUn6yZjKpMSf283IoaK2p76vnQNcBDnUcoLl6H71HDkFjK0ldPpK74V8rLMSm53LhTjOzn/ME1gu3YSnIJ3xOMQ8uuRVjVBS+0h7ULywfbmsPOnZgVYhh4nN5aK1sxNqrMHR4OVx9mHXtO+jFfmwZM0YmJmeRPi+DWdFJlIbPIb0oi/DoyGGvT8J/vNn1JPqFb+Pywfe9X2Kddzl/uHIKqyanMngY7uHm8Xmo6qlif+d+DrTvo33fTgz7Ktmc46YjWrF4r+arz/mOLa8NCpWSxBWn30tkSSnu6dW4V9diLSzElPrh2o0xMafstYjxTfs1vi4nPTXtlJeX09rRRlt/J52+PnzKz1L3ZPINaYQlmkmOTmRyYhKpWWmkF2YTn56IIfgrM/YU1y3hP55s/ws89zX2Gidx08CXOG32TF45axIx4cN7Yo9f+6npraG8vZzytnLK28tprjvIGZtdFDbCGU0amzuw7BnfuIC0c64ke0kY3lnbsGRlYs7Kwpya+r4TkCy5uVhyc4e1biE+yNltp+lQHc3VDbS2ttLW00GeK4lidxp9OHgzbDNhWEiyxTEtLpOUtBQKSouIz00h06AoPXZxw9CT8B8PfB7c2sgfmqfQ572al8zn8bPrZ7BkmM7Q7XZ2s7NtJ+Wtu6nbvwXngf2kNtjJa4aBYivhZ83g4uKLWfZ/j2EonED0xbOJmDYN27RpTMzJCezFJwK5+cNSnxCfxOfy0lLZQHNVA4Y+HxmOWJxN/TzseRl/8LpURgzEm6Ox5kYTN7mQpNRwvh05l+j40fGrU8J/LPP74Z3f4Nz5FJf7/oedzW4umvlFXjy3hBjb0O3tN/Y3sq1lGzsat1JZtZUd1KK05v7f+VgRbN7URgNqQg5nzL6ShDOvDJS3/ZaPPOAqxKmg/RpHSy+q04On2c7r5W/T0NtyrMkGIMMfT1riIiKK4lnimUdsajxpBVkkZCZh/MDgcqPp0yzhP1b1NuH/100YjqzjDf9cusx2/nTNPFaWnNzJWlpravtq2dS0iW0t26jdv5m0fa1Mr9KsrtF0Z0Sz+BffYEbyDDJcmwlLSMI6aRLWwkIMHxh6QIJfnEq+fjeNB+qoO1xDa2sLbb0ddLh7sGkLF7nnAdAX0UeYNYzpcZmkpKWSNiGT1IJMzNbAztISPn7QudFEwn8sqnoT71M34HX08UPPF7GXXsEzayaf8KUUOxwdbGraxMbGDew/+A4HTG0AfPM/Zq7bEbzqRVoyMecvoXDJEpZNWR6YdpP0hRennsfhprWikebqelqbWuns7uJ0dwl6wMs6814qjc2YMBJvjiY/MZvUlDSS5k3DnBLB1dbTQl3+KSPhP8Zov4+OZ2+n227lVuPt3HD5WZw95bP1E3b5XGxt3srG2rep3bYO2/4aiho0qxrgc32avY98h7KJy4jPqsLb2ETkooWYj7bVC3GK+Dw+2o400VzVQCaJ0OZiW105m1wHjrXLKw0xpkgMhVFEZsSyLDyDM5PCSchMPtbLZryS8B8r7J10OXx85/kaylu+xNSCbP5w2VySoz5dv/02extvNbzF1l0v8U7PTjpNTlbsVnzphWBf+tQkohfPIXzmDIomnYsxOhqW5Q7jCxIiwO/34+tx4Wt1UF9Ry5aKHbT3d9Ht7T/WLn+eu4z0hFRSE1OYZTCRkp5KSl4GqfkZmC3vHd8a2eesn1oS/mNB/Vacj1/NVnsu691f59bVi/j8gtyPPUtXa83+zv2sq1vHnu1rSdhUwZyDfq5uhpLr5pJ91fXMWJmLXrkf24zpmFPGzsBuYuRy9dip2Vd17OzXtr4OOj29nO4uJdefRJfqosHaQnxYDDlJmSSnpJCam07GxGzMNiupwLRQv4hRQsJ/NNMa78Y/ol6+nXZ/LE9HXMi/vriA0vTjdzXzaz+723aztnotr9S8QldPMz//s4/Tj15xr7SIpCvP5bKzz8KckRGYlp59al6LGFdcAw6aDtXTXN1Ia0sLqd5Ysnpj6Ozr5inrBgCsmEk0xzA5sYC0wgKSCnNITw1ndvh5Ia5+bJDwH61cffQ/9WUiK5/jVd8MNkz9KXevmYfN8v6uZ0cD/43yZ2l+Yy0T9ncTYzQw6UtLWTHjaxTVbCK6uJSoFcsxp6aG6MWIscrr9tByuBFPm504pw1ncx+PHnmJXr8dgj9MjdqAyVZEQX4OmSmpXEIy6YXZxKTFjft2+eEk4T9KvbC1grKKt3jQcAXFl36f709JPzZPa82e9j28eORF+p5+hjmbujmjCQyALzqC6DPO4EvLfhZY+CdrQvMCxJii/RpftwtPywBbtm2lobWRtv5Oun39+JUmy5fImd5pmBJt5ESnExkTRUp6Kmn5GSTmpmEcNGx4DDkhfCXjh4T/KOM+9Bo/Ko/nsS1NLM55iDuvmE9aTGDwslZ7Ky9veZy2p5/iL1O7URYL3/CmkRkTQ+wFZxK/dAVhpaUyYqU4YVprBlp7aayoo7m2kbb2Vtp7u1AezdmuGQDstexjwOAiwRbLhNgcUtJSyMjPJq0oE2U2chHHHV5enGIS/qOF103vc7cSvfshtOcGvnz6jXx7ZRE+PPyn8kV2Pv8wGa/tZWalxqBh0qLPs3DNzUReFSldMMUJ6Wvrobmyjta6Zrrau5hjLMLbYudVzw6qjK0AWDCRYIkhJSWZ2FkFmFMjuCFhNpbI0I0OKz4dCf/RoKeB7keuILZzJ3/lHFZe8W1SU3v52eb/ZeOuF/neQz2s6QVXjA3LteeRc+UNTMrKCnXVYpTo6+yh8WAdKcRCu4vth8vZ3L0XJ55jy5gwMi0pj8gpicyzzqcsUpFWmEVsarzsXIxSEv4jnPfwetyPX43J4+Rn0d8hc/kk1r90M931VbxTFs6KkuVEn9ZF+hkXEL1s+ftGvhRiML/bh7fFTmNFHbsOltPW00GHsxsHgSFVL3DNIdEUQ2SclfzYbJISE0nOTCUlP4O4jPeGHo4L5YsQQ0bCfwRr7nHyuxcqOMsXzeMpq8gtf4a8Jx9ldi840+L58R0vEBMWC4tDXakYSTwONy0VDTRXN9DS3EJ7VyDk57sKyfIn0mzoYLf5IPGmaPJiMkhKTCYlI5XcSROwpUSTYVDMCfWLEMNOwn8kcnRT+daTXLoTXBHrcXdovvLEegwaPLNKSL/6C4G9fMuJjdUjxga33UlLZSMtNU20NbeQ4oslrS+K9q52/mnZBIDSilhjBMmRCcROySShKI+kZBuz48/FaDJ+whbEWDYk4a+UeghYDbRqrScHp8UDTwK5QDVwqda6SwUaCH8LnA3Ygeu01tuHoo6xQDeV8/Ijl1NR4SWjyEx7XDRFS87Dmmoh+6rrsWTLSVfjjbPfQXNFPf5uNwmuCBxNvTxa+xJ9fsexvvJKw6ywYnKy08iclsi53hhS8zJIyU/HZJGmQPFhQ7Xn/2fg/4C/DJp2G/Ca1voOpdRtwce3AmcBhcHbXOAPwb/jms/v47VHv0nzs68w7YAi22ugKHsxp136K2ym41+HVowtXqcHf7sTT6udt7a9S1NnCx3OHvp1YOTUXF8SK3yBvvLZUWlExUSTnJZMSm46yRPSMYe990twFnmhehlilBiS8Ndar1dK5X5g8ho4ds2yR4A3CYT/GuAvWmsNbFRKxSql0rTWTUNRy2jj8Xn4d9W/8f7XD5lyyEuKSXFo+kRW3f5TJk0qCXV5Yhj4XV4aD9TSUFVHa0sr7T2ddDq7sfksnOcO9IGvttTgNvlIi0wiKS6B5LRUMgoyScxLQ5kMXIIMly1OznC2+acMCvRm4OjIYBlA3aDl6oPT3hf+SqkbgRsBssdgU4ezv4d1D/yYu9J20exs5bJUC67oaCKvv5dLlsnQVGOBvbOf5sP1tNQ20dbaRn9fH0t9U/D1uHjFvJsaYxsGrYg1RpIamURaYgrxMydiTongxvgFGKRNXgyjU3LAV2utlQoOsP3p17kfuB+grKzsM607kvk9Ht75008wP/w02X0+Sq9PpdH3BZ7JKeH+q8uYmS0d6UYTv99Pf0sPzYcbaGtoptichb/NxYbmnezQVceWM2oDcaYojPkRRKSlsSIsFXNiOEm5qe8b2kCIU2U4w7/laHOOUioNaA1ObwAGn4GUGZw2pmmt2fnP++n73R9IanVRm2VBLerm+n4rR5Jm8Mdr5pAaI2dFjlR+r5+O2las/UCnh8qqSjY3ltPl6cOl3jsZKoaFJCcnkZuTS4QhnuSMFFLzMknITn5f75roELwGIQYbzvB/DrgWuCP499lB07+qlHqCwIHenrHe3l/VXcVvN/2Ki3/xOn6LifovzGRp3wts80/ib0W/5MnL5hMme38jgt/tw9vmoKu2ld3799De1UGnvZsubx9e5WeVezqZ/gS8EXYMRgOFibkkJSaSlJFCyoR04tIDJ0Mlh/qFCPEJhqqr5+MEDu4mKqXqgR8SCP2/K6VuAGqAS4OLv0igm2clga6enx+KGkai5v3b2fqbH/DjuXUYw2zM/snnWW2C2Ld+ymv+Gexd8FvuXDVVTo8/xbTWeLodNFbU01bfTHtrOx09nXQ6e5jqyqbAn0q76uUd63YiVBjx1himJKWTlJRMfkkRCbkpZNpMciKUGNWGqrfP5z5i1vLjLKuBrwzFdkcqv8/HW3d/l7g/v0iaEa5fvIrLLvw+Pk8Er9/zJSy+efSffS9fn58f6lLHNL/bS1dtO63VjbQ1tdLe2UGSJ4oJfYnYXQ4eDXsLCPSRjzZGEBcRQ2xJOvETi0hKDKM4aim2yPAQvwohhoec4TvEGg/vZs+3biLrUDcVpbFM/uU93Jw3g+rqCq5+aiftA5fyf5dPY3Vp+ic/mfhE2q9xdvbTWtVEW0MLxn4/mZ4EPK0D/MXxKs5B7fFmjJijCgifWUJ0ko0LnNEk56SRmJ2CWcZEEuOMhP8Q0VrzTOUz+L7+A/LrvdR8+RzO+eqdGIH2x24ksmItVsPdPH7jCqZnxYa63FHHN+Cmo6aV/pZuEjyReNscvFKzgXpXK/04j53pmqpjSU9YjDUnhjLnVGyxESRlppIyIZ3o+Jj3NbFNIyNEr0aI0JPwHwKNzZXcsfkO3ujaxIorSpk985vMKJ0HXjfNf76a1Pr/8GfzZTxw4ypykyJDXe6I5XN66GvoxjIA3nYHOyv2cKSzjm5XH73ajk/5ifLbuMyzAFN8GFarhYzIVBLiE0lKSyI5O7AXb7VaAVjBxBC/IiFGLgn/k6C1Zu2/fkXkHQ8xLdvIvJ98j8snXo5BGcBtp/5Pl5LZ9hYPR9zAeTf/nIRIa6hLDjmf04O3w4mvw0lN1RGq6mvo7O+m29lHrx7Aj+Y61+koFC3hLXQb+omLjiU/No+EpESSs1PJKC1EmQxcwOxQvxwhRi0J/xPk9Dr5x22XMOP5SrqTwlh+yy/InbTy2Pwdf72Nqa1v85fEb3L5TT/40IXVxyqtNX67F1+nk/baZo5U19DZ2UF3f8+xPfhLXPMJx0ql8QjbzUeIMoYTFxlNVkwmCYkJJJRNJSwlikssp4X65QgxZkn4n4BeVy/Pfm0NZeubaVtSyoK7/4wpItCco7Xm169W8KeKJXytYDJf/PwXMBvH1jVztU/j63Ex0NJDY3U9Ha0ddHZ30tXfQ4+7jyXuEhJ0FIeMjaw378eAItoYQUxkNFkxWcROKyA2M5GVUWWcHW7BaBwfX4xCjCQS/p9Ru6OdW/55Azdta6ZnzWmcdscfjx1E1G0HOfjYd3mw6WpWzyrkposuxGgYfX34tdb4Bzy42gdor22hs7Wdro4uunt76Hb2UepMJ90XT4Ohk5csOwACAW+KJC4mhoiJGSTkZjI7soQZthXEJMQduwqUEGJkkPD/DGo7q7np9S/RoTrRD9/FvClnHwt+f+0WHI9cRIIXbprxRb560VQMIzT4tT8Q7u4OOy21TXS1dtDd2U1PXw+9jn5y3YlMcCfTo+w8Zd1wbD0TRmIskRhLY4krKCQyCmJdhSRlpRITGyN78EKMIhL+n9L+5nJ23Hw1Z8Qplt/5CFOTph6b5zv0Kr7Hr6TdF80L0+/laxesCOlZu363D1+PC1+3i8aaBrraO+nu6qanv4deZz8p7mimeLLx4uPPYW8eW8+IgShzBDm52cQUTyA22sz5PQnEZyQTnxRPRETEh15XLKmn+NUJIYaChP+nsKX6bY585WZmHPZi/NZNFA0Kfu++51F/v4YKfyZvz72Pm8+eP2zBr7VGO334el34et34etwcqT5CZ1cnvf299DkG6HfbifOGM89bBMCT1vXHBh4zKxPR5gjSczKILcnHGGfl4u5YYlLiiEtOOG64T5dRaoQYkyT8P8HrB16g6xvfpbTWT/j3byHnyhuOzXN5ffxog2aBbzatS+7kphXTT2gbR3vIeHud9Lf34Oi2E6PD8fW52Vt3kJbedvpdAwx4HQxoF5GEsdoduJjHG5ZNdBj6UUCE0UZkeAThifHEzSjGGG3h4v4kIuKjiE2Iw2azfSjcJ5Nwom+NEGIUk/D/GC8fWUv/177DxAZN/E9/RNqFlx2b567bzs2venm9wkDxufdxw8L3LpuntUa7ffgHvPR19NDd1sVATz/2vgHsAwO4HS5m2grxDXjY3LWXKm8TDtw48YCCcG3lCtciMBk4ZD1Co7+TSHMg2OPDk0hKSCSpbCrGaAuXe4uxhocRERFx3Db3QmJPxVslhBhlVGCctZGtrKxMb9269YTW7W5roaHiIAAKhUahFOSWTiU8OprOlmaaj1SBCszzA8oHYakxfHP9t1hek8bspMVYcvLxerx43V5cnY1Mq9vMDv8MXElpdHsbcXrduLxu3H4PLu3hCtciTBh513SQfab699WkUNwUfy7GSAs7nZU0ezqICI8gIjKSqJgoohJiKJ02GWU14vf75UCqEOKEKKW2aa3LjjdvTO/529sdbL77Dd61HvjQvDOf6ySJOCpULZssFR+af5FrLvfp29ljrOXV1gporXnf/Ezf+RQrG/u7aulQXZgxYMaAzWDFYorANj+NyIQYSlqMpNpTiEqIJSY5gdi0JGLi4o8F+komf+xrkOAXQgyHMR3+yqSIssYzxf/hoZMN1kjsRjORvkSm+H1orUBrNOB2Oanv7aEhyoBbRVPQl4HP48HrceNxO/F6Pbwa5sWgPPjc3eCqxa1duLULrV2gXTxVX4jJbMXrfANX37b3b9toZsm1vyY82krD/nX0tB4hKj6O6KQEIuLiiIyLJ3fazFPzJgkhxqUx3+zzWW04+Crua75GuDmCmS++jjE6cME9v8dN5y+no139vDT7z1y+YjFetx+Py4vb4cPt8OJ2enE7vLgcvmP3+9rb6e9sw9Hfh2ugD5e9H4/LiSlsPgAex9v43QfRfjsQ6JVjMEUyYfathMdYaNj7GANddYTHJhAVn0BsSjLJeTlMXX4mAC67HctxDuQKIcS4bfb5rNrsbVR97xam9UHm3+49Fvxaa37wwkGO9F/DqtmlXLN6GQCWMADLJzxrwYemaK3xOH04+j04+mZh73Vj73XT195Lb0cnA119eFw+mip66O1MxOdx427up6vxADW7t6CMSex4NZLIOCstFX/CNdCKLTqByPgkYlNSyJ5cytTlgXGG3E4HljDbEL5LQoixQMI/yK/9PPXz61m6z4XhK9cROzNwkT7t6uexp57gb3vS+NKS87lqVfFJb0sphcVmwmIzEZM0OJg/PL689s/H0e9hoNtFf5eTvk4nfe19OAagv8uJKWw6Hnczjv5e7L2ttFRVULGlhq3/CSM60UbDnrsAP5FxKcSkppGUnUXejGlkl0790LaEEOOHhH/QX/b+hfr2KnpnFzPnK98BQHvdVN17MZd2b6aj7Em+tqr4lDevKIMiPNpCeLSFpOyo4ywxE79fY+9x0dvhpK/DSW/bAP3dHnra7Fij5uLsb6O3o5vutnJqdr3LrtcqScpzEJ1g5vDmu4hKSCEuLZOUCdkk52STkl9IZFz8KX2dQohTS8If2NO+h99u/y2nX3oGsxffhTIY0F4Xh+69jOKeDTyTfStfuyi0QzZ8HINBERkXRmRc2HFamWbi8/rp63DS0+6gq6mX7pY+Bnqgq7Edv86mq7mTzsYNHN76BgBxGWeQPXUFtigXtbufJSU3l7TCPBKzsolLy8Bk+aSmLiHESDfuD/j2ufp47OYV7Ms38z+3vECMNQa/y07lPRdS1LuB59O/ztlf+J8RO0jbyfL7/PR1OulqttNW20brkRoc/WH0d5vpa6/FM/AC2t8DHP2cKEqW3kjh7LlYwuzYu2tJzptAbGoaBoN0SxViJJEDvh9Ba80Tv76Jxe/2Mr/4QmKsMfj8mif/eh+X92zkhdxbOee6743YPf6hYDAaiEkKJyYpnNwpicCkY/Pcjrl0NZ9DW103TRXVtNfW0N3aQOV2L1U79+B17cZrfxUAZTATGZ9GQkYOCy+/mpS8tMD0MfzeCTGajdvw11rz2NM/puzRHXSXZDLvlv/B4/Pz7b/v4t+VxXjn/Y2r15wzrsPLYjORkhdNSl40kxdnH5vucfvobrbTVltAw4HptFRX0dNaz0B3M30dW2mqnoI1ohKj2oK9axexaTmkFRSQPWUiaQVyPEGIkWBcNvtorfnbQ99m8m/+gycmnGn/eB5tM1H1h0v5Ts9FnLfqLG5a8uETw8THcw546GwcoLOxn/aGAWrLN9HVuAufuwXt7wZAKTOTV/4PSVkxeJyHiU60klUyiYjYuNAWL8QYJM0+g/i1nzs334npzbXkp8dR9rdn8Bl9tP/fCvLdDXx7/g0sl+A/IWERZtILY0kvjA1OKUb7NT1tDpoOt1G79yCd9S00VfZSsaUNV+9jaF8zAGZrDDGpueRMmcHcC87DFikHlYUYTuMq/L0+L7945XYeb3mRa756HXMnfwXHQDu995xFgredTfPvY/mqi0Nd5piiDIrYlHBiU3KYtCDn2HRHn5umw8Uc2bmfpspDdDdV01FXQ2ejnb3vxBMVH4aj+x9EJ6WQXVpK0fyZJGamjetmOCGG0rhp9nF7nDz/tQtI2VrNnrs+zxcX38LuqjrSHltBuK+P3ac/wMKlq4eoYnEinAMeWmt66Ki303ykg4qND+Gx1/PesBdRpBWfwaRFK0jOiSIxMwKz1RzaooUYwT6u2Sdk4a+UWgX8FjACD2it7/ioZU82/J32Pl674Vwm7GiheuUkjAuXc0dDKVVt/Txs/RXhK29n7qIVJ/z8YvjYe5xUbNlH9a7dtFYfQusCPN5c/L523H1PEBaZRUJmIVmlkymeP5WEjDj5dSBE0IgLf6WUETgErATqgS3A57TW+463/MmEf39nKxuuPpfMw70cWGDmguwaOnUkX059nAvKcjl7ShpRYbL3OJr0d7mo2n6I3a89T1djJW5HS3COgcjky8mYWEpCupnU/FgyipKxhI2r1k0hjhmJB3znAJVa6yoApdQTwBrguOF/omo6O3n1K2cz58gADUvtTEuN4o2cb5G//HqeyMr+5CcQI1JknJWpy6cwdfkUAOy9vRzatIsjO3YTkVBMe52Dw5tfxOvcgMGUSmRCIelFU8ifNZn0wgSiEsLk14EY90IV/hlA3aDH9cDcwQsopW4EbgTIzj6xoPYrBw8vsmCaEM2Mc/+b3NnLmGA0nGDJYqQKj45m+srTmL7ytGPT6vbFsuvVOBoOlNPbsp7elnUceNuGNeYmImLDSMoykzkpjYzCOBIyIjDI50KMMyP297DW+n7gfgg0+5zIc+TFZbD2i68TERh7WYwjWSUTySqZCICzv5/aPbtormogLn0iTYd72L/utxx8uxuDKQuzLZe0glKyJ+eRlh9DSl4MZqsMVSHGtlCFfwOQNehxZnDakJPgF2GRkRTNW0jRvMDjKadnkll4ORVbtlK3dxeu3oNUb19L3d7JmMPPwGBQxKdDVkkW6YWxpBXEYrWN2P0kIU5IqD7RW4BCpVQegdC/HLgiRLWIcahk8TJKFi9Da01nQx015bsIj0nAFl1MdXktW5/5Pg17Y1CmdAymDBIyC8meXEBGURzphbFyEpoY9UIS/lprr1Lqq8BaAl09H9Ja7w1FLWJ8U0qRkJlNQuZ7x5WScy1ExXyR2n17qN+3F9fAfloOvUp301mUvzEJ7bcTFe8kZ+oksiclk14YS1ik9BgTo8u4OclLiBOhtaa7uZGGA/vImjwde4+ZrS+8wKF3HweMKGMKBlMaMSn55M2YSXZJSuDLIEK+DETojbh+/p+VhL8YSRx9vdTv30PDgf3UlO+ho/4I2u8lPPHL+H1h+Lw1RMdB3swZ5E3NIq0gVg4gi5CQ8BdiGHndbtrrakjKzqelupeX77+LjtqdAChjMkZLNonZpRTOKSOzOI6UvGiMJulaKoafhL8Qp5Df56OlqpKqHds5vHUb7XUVmG3pGKyXgAbt209ybiaFs6eSXZpEfHqEnHQmhoWEvxAh5HE66e/uxBaVRN2+Nv5995fRfi9gwmBKxxqZS1bpLApnl5A5MZ7IOGuoSxZjhIS/ECPI0WMGVdt3UlO+m772emyxp6PVTLTficV6gJwp0yleMIWMonjMFjleIE6MhL8QI5ijrxetwd6r2PHy2+xee09ghgrDaMkhPmMixQsWkj8jm4QMaSISn56EvxCjSH9XJ0d27uDghi00HizH4+zBEn0dBmM8Fms78ekwaeEcJkzPkPMLxMeS8BdilNJa09lYj8WWRP2BTjY8dT9djVsBAwZTOtHJxeTNmMWkRVNJyY3BYJBfBeI9Ev5CjBE+r4f6A/vZt34DNbt3MNBVjzLEYo25HmuEiYS0XgpmFVNQloMtSoagGO9G4nj+QogTYDSZyZk8lZzJUwEY6O6irbYRtyuBmvJ2yl/5HVVbXLxiSiM6aSITZpZRcto0UnKiUfKrQAwie/5CjBHa76epsoK9696hasdW+jtqATCGzSU6+XSyJsWSURxF/owMrOFyrGA8kD1/IcYBZTCQXlRMelExcD0D3V0c2rgZrzeerpZwDm/fye61T2IwZxKXPpmiufOYtLCY2JRw6UE0DsmevxDjRGdjAxuffo7qXVtx9Aaue6wMCcRnX0b+rAJyJyeSXhQrQ0+MIXLAVwjxPt3NTex7+10qt2wjLuNiGip6cfVuANVFav50ShbPo2BWply3YJST8BdCfCyP28fa+x6gcvNr+Dx2Al1Js4jPnMGUZSvJnZpIXKo0D402Ev5CiE/F7/fReGA/5W++zZEdm0EloQ1nAmAylZM7bSqTl0wjLT9WLno/Ckj4CyFOiMftwtmvOfBuBev/+t+ABkMUlvAiskrKmHz6bHImJ8n1CkYoCX8hxEmz9/ZwaMMG9q57m5Yje9B+L+aIs7CEl5JeFE7elCTyZ2UQHi3HCUYKCX8hxJByOx1U7diGJSyHhkMO9q57kYGONzGYc4nPnMqkRQsonptDTJIt1KWOaxL+Qohh1VR5kG0v/IcjOzbjdvQACoMpm9Tiq5gwI4UJ05JIzIqUA8anmIS/EOKU0H4/zVUV7F33Fq3VLdhiz6Kpshv3wBuERcRSMHs+JYtLSJ0gg9CdChL+QoiQGehx8MQPbqW7uQoAZUzEGllM/qyFlJxWSkZRnJxYNkxkeAchRMhExNi44be/o7e9lQPvvMPe9evprH+Xii0mDu/wYgnzk5LjpfT0meSUJmCSK5edErLnL4Q45Qa6u9Ba0VbnZdsLa6nZ+TgYojCHFZNVOofSJTPJnZKIJUz2T0+G7PkLIUaUiNg4ACLjIG3CGiq2JFP++jqaK3dQtWUrVVujCE+4mpzJmeTPTCZ3aiJWm8TVUJJ3UwgRUmGRkUxZupIpS1fisg9QuWUTlVt3EZdZQNWONg5t+CdK+UkrLmPK6WVMmJ4sQ1IPAWn2EUKMWNqv+dcvfkH1rg1ovxdUBCZrEekT5zJ5ySzypiUSFiFfBB9l2Jp9lFKXAD8CJgFztNZbB837HnAD4AO+rrVeG5y+CvgtYAQe0FrfcTI1CCHGLmVQXHjbrbjsdg5v28Se19dRf2AnbUfMvF4TjlKahIxeJi+ZTf7MFPki+AxOas9fKTUJ8AN/BG45Gv5KqRLgcWAOkA68ChQFVzsErATqgS3A57TW+z5uO7LnL4Q4yu2w43G76e8ysP3Ft9m37t5jvwjSimYzZeksaRoKGrY9f631/uAGPjhrDfCE1toFHFFKVRL4IgCo1FpXBdd7Irjsx4a/EEIcZbGFY7GFExEDy69fSt60SMpff4O6fTuo272DuvIowmIvIXdKPgVlKeRNTcQiB4s/ZLjekQxg46DH9cFpAHUfmD53mGoQQoxxljAbExcuZuLCxbjsdiq3bmT/2xtIKSihakcHlVteRikXmSVzmHL6NHKnJsoIpEGfGP5KqVeB1OPMul1r/ezQl3RsuzcCNwJkZ2cP12aEEGOENTyc0sXLKF28DIBFF2ueuesdqrZt4sjWjVRvT8Bsm0jOtPlMXlxK9uR4TObx+0XwieGvtV5xAs/bAGQNepwZnMbHTP/gdu8H7odAm/8J1CCEGMeUQXHBd7/LQPcXObTxHcpff4O2mneo2dlF3X4/5jAjWcUWJi0qJGtS/LgbYmK4mn2eAx5TSt1N4IBvIbAZUEChUiqPQOhfDlwxTDUIIQQRsXHMWLWaGatW09fRjsfpZKAnnN1v7mLfmz9l/1vpWKMmUThnESWLCkgvjB0Xg86dbFfPC4DfA0nAC0qpnVrrM7XWe5VSfydwINcLfEVr7Quu81VgLYGung9prfee1CsQQohPKSohEYD4DIhPn058ytXsefMNeltfo/zl19n7ehZRKWcxcd5ECmenkJwbNWaHoZaTvIQQ415HfS1717/JgXfeIW3iDdQfHMBjr8QWBSWnLWTigmwS0iNDXeZnJkM6CyHEZ+Cye3jyxz+grbocMGOw5BOXNp3JSxdQPDedqPiwUJf4qUj4CyHEZ+T3+2jYv5c9b75BxaZ38bgGMJjzsUSuIa0ghsLZKRTMSsYWOXKvWSzhL4QQJ8Hn9VC9awdup2KgJ4n97x6m7fCDGC1FZJTMZcrS6UyYnox5hF2LQIZ0FkKIk2A0mcmfNefY4wnTTLzyQAEN+7dTs30rtTvjMYdPomD2ckoX55NZHIfBOLK7jsqevxBCnCBHXy+HNrzNrtdep636IJHJN+L1RGCx9TFhegZTTi8iKTt0PYak2UcIIYaZvacbS3gUNXs6ePWBX9LffgCDKYvIxKlMWbqYkkV5RCfaTmlNEv5CCHEKdTTUseeN19m3/k3sPW2AEaN1OtlTz6N4bir5M5NPyfDTEv5CCBECWmuaKw+x+7XXcNojGOgtprOpF59zHRmT5jDjjHnkTknCaB6e4wNywFcIIUJAKUVaYTFphcVA4MvgwLu7+M89+6nZsYuanY9jiSihoOw0pp8xg9QJMafs+IDs+QshxCnmcTqp2LKBHWtfpbmyHLQfS9RVxKXnUDwnieJ5GUNyfECafYQQYoSy9/ZQsWkT5vBSDm1qoXrHU/h97cRnzmTayqWULio44YvRSPgLIcQosfHpZ9j58ksMdDUAiuTCr3D1/646oeeSNn8hhBgl5l14PvMuPJ+22mr2vLGBvJnHze6TJuEvhBAjUFJ2LkuvzR225x/Z5x8LIYQYFhL+QggxDkn4CyHEOCThL4QQ45CEvxBCjEMS/kIIMQ5J+AshxDgk4S+EEOPQqBjeQSnVBtScwKqJQPsQlzNUpLYTI7WdGKntxIz22nK01knHmzEqwv9EKaW2ftS4FqEmtZ0Yqe3ESG0nZizXJs0+QggxDkn4CyHEODTWw//+UBfwMaS2EyO1nRip7cSM2drGdJu/EEKI4xvre/5CCCGOQ8JfCCHGoTEZ/kqpVUqpg0qpSqXUbSHY/kNKqVal1J5B0+KVUq8opSqCf+OC05VS6nfBWncrpWYOc21ZSqk3lFL7lFJ7lVLfGCn1KaXClFKblVK7grX9ODg9Tym1KVjDk0opS3C6Nfi4Mjg/d7hqG1SjUSm1Qyn1/EiqTSlVrZQqV0rtVEptDU4L+b9pcHuxSql/KKUOKKX2K6Xmj4TalFLFwffr6K1XKfXNkVBbcHvfCv4/2KOUejz4/2PoPm9a6zF1A4zAYWACYAF2ASWnuIbFwExgz6BpvwBuC96/DbgzeP9s4CVAAfOATcNcWxowM3g/CjgElIyE+oLbiAzeNwObgtv8O3B5cPp9wM3B+18G7gvevxx48hT8234beAx4Pvh4RNQGVAOJH5gW8n/T4PYeAb4QvG8BYkdKbYNqNALNQM5IqA3IAI4AtkGfs+uG8vM27G/qqb4B84G1gx5/D/heCOrI5f3hfxBIC95PAw4G7/8R+NzxljtFdT4LrBxp9QHhwHZgLoGzGE0f/PcF1gLzg/dNweXUMNaUCbwGLAOeD4bASKmtmg+Hf8j/TYGYYIipkVbbB+o5A3hnpNRGIPzrgPjg5+d54Myh/LyNxWafo2/aUfXBaaGWorVuCt5vBlKC90NWb/Cn4QwCe9gjor5gs8pOoBV4hcCvuG6ttfc42z9WW3B+D5AwXLUBvwG+C/iDjxNGUG0aeFkptU0pdWNw2kj4N80D2oCHg81lDyilIkZIbYNdDjwevB/y2rTWDcBdQC3QRODzs40h/LyNxfAf8XTg6zmkfWyVUpHAP4Fvaq17B88LZX1aa5/WejqBvew5wMRQ1PFBSqnVQKvWeluoa/kIi7TWM4GzgK8opRYPnhnCf1MTgSbQP2itZwADBJpSRkJtAATbzc8DnvrgvFDVFjzOsIbAl2c6EAGsGsptjMXwbwCyBj3ODE4LtRalVBpA8G9rcPopr1cpZSYQ/I9qrZ8eafUBaK27gTcI/LSNVUqZjrP9Y7UF58cAHcNU0kLgPKVUNfAEgaaf346Q2o7uKaK1bgX+ReCLcyT8m9YD9VrrTcHH/yDwZTASajvqLGC71rol+Hgk1LYCOKK1btNae4CnCXwGh+zzNhbDfwtQGDwqbiHwc+65ENcEgRquDd6/lkBb+9Hp1wR7EswDegb95BxySikFPAjs11rfPZLqU0olKaVig/dtBI5F7CfwJXDxR9R2tOaLgdeDe2pDTmv9Pa11ptY6l8Bn6nWt9ZUjoTalVIRSKurofQLt13sYAf+mWutmoE4pVRyctBzYNxJqG+RzvNfkc7SGUNdWC8xTSoUH/88efd+G7vM23AdSQnEjcFT+EIH24ttDsP3HCbTTeQjs+dxAoP3tNaACeBWIDy6rgHuCtZYDZcNc2yICP2N3AzuDt7NHQn3AVGBHsLY9wA+C0ycAm4FKAj/NrcHpYcHHlcH5E07Rv+/pvNfbJ+S1BWvYFbztPfqZHwn/psHtTQe2Bv9dnwHiRlBtEQT2kGMGTRsptf0YOBD8v/BXwDqUnzcZ3kEIIcahsdjsI4QQ4hNI+AshxDgk4S+EEOOQhL8QQoxDEv5CCDEOSfgLIcQ4JOEvhBDj0P8Hc0qwoMdBGukAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "for mpid in df_predictions.index:\n",
+    "    temp = range(5,801,20)\n",
+    "    S_true = df.loc[mpid,['S_{}'.format(T) for T in temp]].values\n",
+    "    S_pred = df_predictions.loc[mpid,['S_{}_atom'.format(T) for T in temp]].values*df.loc[mpid,'natoms']\n",
+    "    \n",
+    "    C_v_true = df.loc[mpid,['C_v_{}'.format(T) for T in temp]].values\n",
+    "    C_v_pred = df_predictions.loc[mpid,['C_v_{}_atom'.format(T) for T in temp]].values*df.loc[mpid,'natoms']\n",
+    "    \n",
+    "    H_true = df.loc[mpid,['H_{}'.format(T) for T in temp]].values/1000\n",
+    "    H_pred = df_predictions.loc[mpid,['H_{}_atom'.format(T) for T in temp]].values*df.loc[mpid,'natoms']/1000\n",
+    "    \n",
+    "    U_true = df.loc[mpid,['U_{}'.format(T) for T in temp]].values/1000\n",
+    "    U_pred = df_predictions.loc[mpid,['U_{}_atom'.format(T) for T in temp]].values*df.loc[mpid,'natoms']/1000\n",
+    "    \n",
+    "    fig,ax = plt.subplots()\n",
+    "    ax.set_title(mpid)\n",
+    "    ax.plot(temp,S_true)\n",
+    "    ax.plot(temp,S_pred,'--')\n",
+    "    \n",
+    "    ax.plot(temp,C_v_true)\n",
+    "    ax.plot(temp,C_v_pred,'--')\n",
+    "    \n",
+    "    ax.plot(temp,H_true)\n",
+    "    ax.plot(temp,H_pred,'--')\n",
+    "    \n",
+    "    ax.plot(temp,U_true)\n",
+    "    ax.plot(temp,U_pred,'--')"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (matenv)",
+   "display_name": "Python [conda env:modnet-develop]",
    "language": "python",
-   "name": "matenv"
+   "name": "conda-env-modnet-develop-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -494,7 +1508,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/example_notebooks/training_ref_index.ipynb b/example_notebooks/training_ref_index.ipynb
index 4c0565fb..98054c43 100644
--- a/example_notebooks/training_ref_index.ipynb
+++ b/example_notebooks/training_ref_index.ipynb
@@ -131,13 +131,6 @@
    "execution_count": 3,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Loaded DeBreuck2020Featurizer featurizer.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -150,12 +143,17 @@
       "\"Statistical analysis of coordination environments in oxides\",\n",
       "Chem. Mater., 2017, 29 (19), pp 8346-8360,\n",
       "DOI: 10.1021/acs.chemmater.7b02766\n",
-      "\n"
+      "\n",
+      "2021-02-24 14:27:48,216 - modnet - INFO - Loaded DeBreuck2020Featurizer featurizer.\n"
      ]
     }
    ],
    "source": [
-    "md = MODData(df['structure'],df['ref_index'].values,structure_ids = df.index, target_names = ['refractive_index'])"
+    "md = MODData(materials = df['structure'],\n",
+    "             targets = df['ref_index'].values,\n",
+    "             structure_ids = df.index,\n",
+    "             target_names = ['refractive_index']\n",
+    "            )"
    ]
   },
   {
@@ -172,18 +170,20 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:root:Computing features, this can take time...\n",
-      "INFO:root:Fast featurization on, retrieving from database...\n",
-      "INFO:root:Retrieved features for 4022 out of 4022 materials\n",
-      "INFO:root:Data has successfully been featurized!\n"
+      "2021-02-24 14:27:48,222 - modnet - INFO - Computing features, this can take time...\n",
+      "2021-02-24 14:27:48,223 - modnet - INFO - Fast featurization on, retrieving from database...\n",
+      "2021-02-24 14:27:52,091 - modnet - INFO - Retrieved features for 4022 out of 4022 materials\n",
+      "2021-02-24 14:27:53,354 - modnet - INFO - Data has successfully been featurized!\n"
      ]
     }
    ],
    "source": [
-    "md.featurize(fast=True,db_file='../modnet/data/feature_database.pkl')"
+    "md.featurize(fast=True,\n",
+    "             db_file='../modnet/data/feature_database.pkl'\n",
+    "            )"
    ]
   },
   {
@@ -528,44 +528,49 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:root:Loading cross NMI from 'Features_cross' file.\n",
-      "INFO:root:Starting target 1/1: refractive_index ...\n",
-      "INFO:root:Computing mutual information between features and target...\n",
-      "INFO:root:Computing optimal features...\n",
-      "INFO:root:Selected 50/1019 features...\n",
-      "INFO:root:Selected 100/1019 features...\n",
-      "INFO:root:Selected 150/1019 features...\n",
-      "INFO:root:Selected 200/1019 features...\n",
-      "INFO:root:Selected 250/1019 features...\n",
-      "INFO:root:Selected 300/1019 features...\n",
-      "INFO:root:Selected 350/1019 features...\n",
-      "INFO:root:Selected 400/1019 features...\n",
-      "INFO:root:Selected 450/1019 features...\n",
-      "INFO:root:Selected 500/1019 features...\n",
-      "INFO:root:Selected 550/1019 features...\n",
-      "INFO:root:Selected 600/1019 features...\n",
-      "INFO:root:Selected 650/1019 features...\n",
-      "INFO:root:Selected 700/1019 features...\n",
-      "INFO:root:Selected 750/1019 features...\n",
-      "INFO:root:Selected 800/1019 features...\n",
-      "INFO:root:Selected 850/1019 features...\n",
-      "INFO:root:Selected 900/1019 features...\n",
-      "INFO:root:Selected 950/1019 features...\n",
-      "INFO:root:Selected 1000/1019 features...\n",
-      "INFO:root:Done with target 1/4022: refractive_index.\n",
-      "INFO:root:Merging all features...\n",
-      "INFO:root:Done.\n"
+      "2021-02-24 14:27:53,399 - modnet - INFO - Loading cross NMI from 'Features_cross' file.\n",
+      "2021-02-24 14:27:53,444 - modnet - WARNING - Feature mismatch between precomputed `Features_cross` and `df_featurized`. Missing columns: {'BondFractions|Cd - Zn bond frac.', 'BondFractions|La - Np bond frac.', 'BondFractions|Dy - P bond frac.', 'BondFractions|Ga - Sn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 143', 'CoulombMatrix|coulomb matrix eig 213', 'BondFractions|Hg - Lu bond frac.', 'BondFractions|Ir - Yb bond frac.', 'BondFractions|Pa - W bond frac.', 'CoulombMatrix|coulomb matrix eig 12', 'BondFractions|N - Re bond frac.', 'BondFractions|Mo - S bond frac.', 'BondFractions|Ge - Pt bond frac.', 'BondFractions|K - Pb bond frac.', 'BondFractions|Li - Yb bond frac.', 'CoulombMatrix|coulomb matrix eig 260', 'BondFractions|La - Ta bond frac.', 'BondFractions|Al - Cd bond frac.', 'BondFractions|Os - Os bond frac.', 'BondFractions|Ag - Np bond frac.', 'BondFractions|Nb - Pd bond frac.', 'BondFractions|Os - Tc bond frac.', 'BondFractions|B - Sb bond frac.', 'BondFractions|H - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 125', 'BondFractions|Np - Ti bond frac.', 'BondFractions|Ce - Cr bond frac.', 'BondFractions|Eu - Eu bond frac.', 'BondFractions|Np - Zr bond frac.', 'BondFractions|Hg - Ni bond frac.', 'BondFractions|Ba - Ru bond frac.', 'BondFractions|Ho - Y bond frac.', 'BondFractions|Fe - Y bond frac.', 'BondFractions|Al - Os bond frac.', 'BondFractions|O - Pu bond frac.', 'CoulombMatrix|coulomb matrix eig 172', 'BondFractions|Na - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 202', 'BondFractions|Os - Re bond frac.', 'BondFractions|Cl - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 249', 'BondFractions|Rh - S bond frac.', 'BondFractions|As - Cl bond frac.', 'BondFractions|Ca - Te bond frac.', 'BondFractions|Hg - Y bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 82', 'BondFractions|Gd - Mg bond frac.', 'BondFractions|V - Xe bond frac.', 'BondFractions|Br - Tl bond frac.', 'BondFractions|In - Ru bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 202', 'BondFractions|Dy - Th bond frac.', 'BondFractions|As - H bond frac.', 'BondFractions|Rh - Te bond frac.', 'BondFractions|Se - Tm bond frac.', 'BondFractions|Nd - Te bond frac.', 'BondFractions|Ac - Bi bond frac.', 'BondFractions|Ac - Mg bond frac.', 'XRDPowderPattern|xrd_52', 'BondFractions|As - Mo bond frac.', 'BondFractions|Ho - Re bond frac.', 'BondFractions|Sb - Sb bond frac.', 'BondFractions|Ac - Br bond frac.', 'CoulombMatrix|coulomb matrix eig 290', 'SineCoulombMatrix|sine coulomb matrix eig 212', 'BondFractions|Ag - Ho bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 206', 'BondFractions|Ag - Co bond frac.', 'BondFractions|As - Pu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 247', 'CoulombMatrix|coulomb matrix eig 18', 'BondFractions|La - V bond frac.', 'BondFractions|H - Hg bond frac.', 'BondFractions|Ho - Ti bond frac.', 'BondFractions|Pu - Pu bond frac.', 'XRDPowderPattern|xrd_58', 'CoulombMatrix|coulomb matrix eig 159', 'BondFractions|Au - Pm bond frac.', 'CoulombMatrix|coulomb matrix eig 256', 'BondFractions|Bi - Pr bond frac.', 'BondFractions|H - N bond frac.', 'BondFractions|Au - Sr bond frac.', 'BondFractions|Hg - V bond frac.', 'BondFractions|Rh - Sm bond frac.', 'BondFractions|Pr - Pt bond frac.', 'BondFractions|Ag - Sc bond frac.', 'BondFractions|H - Pt bond frac.', 'CoulombMatrix|coulomb matrix eig 160', 'BondFractions|Pm - Sm bond frac.', 'BondFractions|Hg - Mo bond frac.', 'BondFractions|Bi - Mg bond frac.', 'BondFractions|U - W bond frac.', 'BondFractions|Ba - Ga bond frac.', 'BondFractions|H - Rb bond frac.', 'BondFractions|Sm - Th bond frac.', 'CoulombMatrix|coulomb matrix eig 115', 'SineCoulombMatrix|sine coulomb matrix eig 19', 'SineCoulombMatrix|sine coulomb matrix eig 167', 'BondFractions|He - O bond frac.', 'BondFractions|Ni - Pm bond frac.', 'BondFractions|Pr - Re bond frac.', 'BondFractions|Dy - K bond frac.', 'BondFractions|Hg - Pu bond frac.', 'BondFractions|Pt - S bond frac.', 'BondFractions|Hg - In bond frac.', 'BondFractions|Na - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 43', 'BondFractions|Au - Cu bond frac.', 'BondFractions|La - Mg bond frac.', 'BondFractions|Lu - Lu bond frac.', 'CoulombMatrix|coulomb matrix eig 43', 'BondFractions|Pa - Pb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 117', 'BondFractions|In - Pb bond frac.', 'BondFractions|Pt - Te bond frac.', 'BondFractions|Cu - Zr bond frac.', 'BondFractions|Ac - F bond frac.', 'BondFractions|W - Xe bond frac.', 'BondFractions|Sr - Zr bond frac.', 'CoulombMatrix|coulomb matrix eig 154', 'BondFractions|B - Lu bond frac.', 'BondFractions|Nd - Zr bond frac.', 'BondFractions|C - La bond frac.', 'BondFractions|Be - Te bond frac.', 'BondFractions|Br - Ga bond frac.', 'BondFractions|Tl - Tm bond frac.', 'BondFractions|Au - Th bond frac.', 'BondFractions|Pb - Pu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 36', 'BondFractions|Se - Y bond frac.', 'BondFractions|Sm - U bond frac.', 'BondFractions|Eu - Pb bond frac.', 'CoulombMatrix|coulomb matrix eig 278', 'BondFractions|Se - Yb bond frac.', 'CoulombMatrix|coulomb matrix eig 123', 'BondFractions|Mg - Tc bond frac.', 'BondFractions|Np - Tc bond frac.', 'BondFractions|As - Pb bond frac.', 'BondFractions|Rb - Ta bond frac.', 'CoulombMatrix|coulomb matrix eig 250', 'BondFractions|Pd - V bond frac.', 'BondFractions|Be - Yb bond frac.', 'BondFractions|Ho - Mn bond frac.', 'BondFractions|Ga - Hf bond frac.', 'BondFractions|Cd - Ce bond frac.', 'BondFractions|Hf - Mo bond frac.', 'BondFractions|Ba - Gd bond frac.', 'BondFractions|Ge - Sb bond frac.', 'BondFractions|Cd - Dy bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 244', 'BondFractions|H - Os bond frac.', 'BondFractions|Pr - Pr bond frac.', 'BondFractions|P - Tb bond frac.', 'BondFractions|As - Ni bond frac.', 'BondFractions|I - Tm bond frac.', 'BondFractions|Ce - Zn bond frac.', 'BondFractions|H - Nd bond frac.', 'CoulombMatrix|coulomb matrix eig 56', 'BondFractions|Ag - Al bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 259', 'BondFractions|Eu - Mn bond frac.', 'BondFractions|Pb - Pr bond frac.', 'BondFractions|In - Tb bond frac.', 'BondFractions|Au - Re bond frac.', 'BondFractions|Cd - V bond frac.', 'BondFractions|Ho - Tm bond frac.', 'BondFractions|Li - Rb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 146', 'BondFractions|Dy - Zn bond frac.', 'BondFractions|Be - Th bond frac.', 'BondFractions|C - I bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 51', 'BondFractions|Au - Hf bond frac.', 'BondFractions|Gd - Tb bond frac.', 'BondFractions|Cr - Tl bond frac.', 'BondFractions|As - C bond frac.', 'BondFractions|Sb - Ta bond frac.', 'BondFractions|Bi - Re bond frac.', 'BondFractions|Ce - Ir bond frac.', 'BondFractions|Tc - Zr bond frac.', 'BondFractions|Np - Sn bond frac.', 'BondFractions|Ho - Zr bond frac.', 'BondFractions|In - Yb bond frac.', 'BondFractions|Pa - Pa bond frac.', 'BondFractions|Cl - Pa bond frac.', 'BondFractions|Nd - U bond frac.', 'BondFractions|Si - Zr bond frac.', 'BondFractions|Cu - In bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 56', 'BondFractions|Li - Tc bond frac.', 'BondFractions|Br - Pm bond frac.', 'BondFractions|As - Mn bond frac.', 'CoulombMatrix|coulomb matrix eig 55', 'BondFractions|Ce - Te bond frac.', 'BondFractions|Cs - Hf bond frac.', 'BondFractions|Ba - Cs bond frac.', 'BondFractions|Ag - Zr bond frac.', 'BondFractions|Ho - In bond frac.', 'BondFractions|Pa - Ta bond frac.', 'BondFractions|Hf - Li bond frac.', 'BondFractions|Cd - Mg bond frac.', 'BondFractions|Ho - P bond frac.', 'BondFractions|Ag - Nb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 57', 'BondFractions|La - Pt bond frac.', 'BondFractions|F - H bond frac.', 'BondFractions|Ag - Ce bond frac.', 'BondFractions|As - Cd bond frac.', 'CoulombMatrix|coulomb matrix eig 207', 'BondFractions|Ac - Cd bond frac.', 'BondFractions|Ag - Te bond frac.', 'BondFractions|Os - Yb bond frac.', 'CoulombMatrix|coulomb matrix eig 226', 'BondFractions|Eu - Pd bond frac.', 'BondFractions|Mn - Sm bond frac.', 'BondFractions|Cl - I bond frac.', 'CoulombMatrix|coulomb matrix eig 27', 'BondFractions|Ni - Rh bond frac.', 'BondFractions|Hg - Li bond frac.', 'BondFractions|Ag - Cd bond frac.', 'BondFractions|Se - Ti bond frac.', 'BondFractions|Mo - W bond frac.', 'BondFractions|Nd - Yb bond frac.', 'BondFractions|In - Np bond frac.', 'BondFractions|Rb - Sc bond frac.', 'XRDPowderPattern|xrd_126', 'SineCoulombMatrix|sine coulomb matrix eig 20', 'BondFractions|Cs - Ta bond frac.', 'BondFractions|Ce - Sr bond frac.', 'BondFractions|Eu - S bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 52', 'BondFractions|Sn - Yb bond frac.', 'BondFractions|Mn - Y bond frac.', 'BondFractions|Ge - S bond frac.', 'BondFractions|H - Y bond frac.', 'BondFractions|Yb - Zn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 63', 'BondFractions|Br - Hf bond frac.', 'BondFractions|Au - Lu bond frac.', 'BondFractions|Cd - Pb bond frac.', 'BondFractions|H - S bond frac.', 'BondFractions|C - Pr bond frac.', 'BondFractions|S - W bond frac.', 'BondFractions|Dy - Ge bond frac.', 'CoulombMatrix|coulomb matrix eig 68', 'BondFractions|Ce - Cl bond frac.', 'BondFractions|Ce - F bond frac.', 'CoulombMatrix|coulomb matrix eig 237', 'BondFractions|O - Re bond frac.', 'BondFractions|Cr - Ga bond frac.', 'BondFractions|Er - K bond frac.', 'BondFractions|Br - Sr bond frac.', 'BondFractions|Co - Zr bond frac.', 'BondFractions|Cu - Hg bond frac.', 'BondFractions|Au - Tm bond frac.', 'BondFractions|O - Sb bond frac.', 'BondFractions|Bi - Ni bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 223', 'BondFractions|Br - Nb bond frac.', 'BondFractions|H - Mn bond frac.', 'BondFractions|O - Ru bond frac.', 'CoulombMatrix|coulomb matrix eig 251', 'BondFractions|Gd - Ge bond frac.', 'BondFractions|Hf - O bond frac.', 'BondFractions|Ag - Mn bond frac.', 'BondFractions|Pm - Zn bond frac.', 'CoulombMatrix|coulomb matrix eig 239', 'CoulombMatrix|coulomb matrix eig 245', 'BondFractions|Mg - Zr bond frac.', 'BondFractions|Bi - Tb bond frac.', 'BondFractions|Pb - Ta bond frac.', 'XRDPowderPattern|xrd_122', 'BondFractions|Cs - Na bond frac.', 'BondFractions|In - Y bond frac.', 'BondFractions|Ac - Fe bond frac.', 'BondFractions|Br - Ge bond frac.', 'BondFractions|Np - Pt bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 103', 'BondFractions|Mg - Pd bond frac.', 'BondFractions|Si - Th bond frac.', 'BondFractions|Br - Y bond frac.', 'BondFractions|Rb - Sn bond frac.', 'CoulombMatrix|coulomb matrix eig 222', 'BondFractions|Cr - Ta bond frac.', 'BondFractions|Cl - Pt bond frac.', 'BondFractions|Ho - Se bond frac.', 'BondFractions|Ru - Ti bond frac.', 'BondFractions|Sb - Th bond frac.', 'BondFractions|Tc - Ti bond frac.', 'BondFractions|Bi - Sm bond frac.', 'BondFractions|K - Lu bond frac.', 'BondFractions|Eu - Sm bond frac.', 'BondFractions|Au - Zr bond frac.', 'BondFractions|Ag - Tc bond frac.', 'BondFractions|Rb - Zn bond frac.', 'BondFractions|Bi - Eu bond frac.', 'BondFractions|Ir - Tl bond frac.', 'BondFractions|C - Sb bond frac.', 'BondFractions|K - Zr bond frac.', 'BondFractions|Ru - Zr bond frac.', 'XRDPowderPattern|xrd_62', 'CoulombMatrix|coulomb matrix eig 14', 'BondFractions|Sb - Tm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 204', 'BondFractions|H - Pa bond frac.', 'BondFractions|Pu - Yb bond frac.', 'BondFractions|Ir - Mg bond frac.', 'CoulombMatrix|coulomb matrix eig 84', 'BondFractions|Pb - W bond frac.', 'BondFractions|Er - Rb bond frac.', 'BondFractions|Ho - Pt bond frac.', 'BondFractions|Hg - Pt bond frac.', 'BondFractions|Cr - Pr bond frac.', 'BondFractions|I - V bond frac.', 'BondFractions|Cs - Yb bond frac.', 'BondFractions|Hg - Re bond frac.', 'BondFractions|Al - Ir bond frac.', 'BondFractions|Cr - Lu bond frac.', 'BondFractions|Ni - Tb bond frac.', 'BondFractions|Pb - Sc bond frac.', 'BondFractions|B - Os bond frac.', 'BondFractions|Br - Th bond frac.', 'BondFractions|Nd - Ta bond frac.', 'CoulombMatrix|coulomb matrix eig 227', 'SineCoulombMatrix|sine coulomb matrix eig 55', 'BondFractions|Hg - Pd bond frac.', 'BondFractions|Ge - Os bond frac.', 'BondFractions|H - Re bond frac.', 'BondFractions|Cd - Mn bond frac.', 'BondFractions|Ho - Pu bond frac.', 'BondFractions|Ga - Y bond frac.', 'BondFractions|Mg - Rb bond frac.', 'BondFractions|Ga - Nd bond frac.', 'BondFractions|Fe - H bond frac.', 'BondFractions|Cs - Pd bond frac.', 'BondFractions|Sc - Ta bond frac.', 'BondFractions|O - Sr bond frac.', 'BondFractions|Dy - H bond frac.', 'BondFractions|Cd - I bond frac.', 'BondFractions|In - Pr bond frac.', 'BondFractions|Cl - Pb bond frac.', 'BondFractions|Ce - O bond frac.', 'BondFractions|Ho - Mo bond frac.', 'CoulombMatrix|coulomb matrix eig 234', 'CoulombMatrix|coulomb matrix eig 83', 'BondFractions|F - Sn bond frac.', 'CoulombMatrix|coulomb matrix eig 133', 'BondFractions|As - Tm bond frac.', 'BondFractions|Cs - Th bond frac.', 'BondFractions|Pa - Sc bond frac.', 'BondFractions|Bi - Cl bond frac.', 'BondFractions|Ac - Li bond frac.', 'BondFractions|Ga - Se bond frac.', 'BondFractions|N - Te bond frac.', 'BondFractions|Co - Sb bond frac.', 'CoulombMatrix|coulomb matrix eig 128', 'BondFractions|Ac - K bond frac.', 'BondFractions|Pa - U bond frac.', 'BondFractions|Ba - Lu bond frac.', 'XRDPowderPattern|xrd_6', 'BondFractions|Ba - Th bond frac.', 'CoulombMatrix|coulomb matrix eig 224', 'SineCoulombMatrix|sine coulomb matrix eig 152', 'BondFractions|Pb - Tb bond frac.', 'BondFractions|Ce - Ce bond frac.', 'CoulombMatrix|coulomb matrix eig 96', 'BondFractions|Ce - Gd bond frac.', 'BondFractions|Th - Tm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 174', 'BondFractions|Cs - La bond frac.', 'BondFractions|Ac - Si bond frac.', 'BondFractions|B - Re bond frac.', 'BondFractions|In - Pt bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 175', 'BondFractions|Be - Os bond frac.', 'BondFractions|Ge - Pu bond frac.', 'BondFractions|Mo - Th bond frac.', 'BondFractions|Li - Sr bond frac.', 'XRDPowderPattern|xrd_92', 'BondFractions|Cd - Tc bond frac.', 'BondFractions|Sb - U bond frac.', 'BondFractions|As - Br bond frac.', 'BondFractions|Cl - Np bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 154', 'BondFractions|Lu - Sr bond frac.', 'BondFractions|Ag - Os bond frac.', 'BondFractions|Ir - Na bond frac.', 'BondFractions|Ca - Ta bond frac.', 'BondFractions|Br - Pr bond frac.', 'BondFractions|Ba - Br bond frac.', 'BondFractions|Ce - Os bond frac.', 'BondFractions|Cu - Dy bond frac.', 'BondFractions|Ca - H bond frac.', 'BondFractions|Ba - Dy bond frac.', 'BondFractions|Dy - Na bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 253', 'BondFractions|Al - Hf bond frac.', 'BondFractions|Bi - La bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 289', 'BondFractions|Ca - Pr bond frac.', 'BondFractions|Ce - Lu bond frac.', 'BondFractions|B - Pr bond frac.', 'CoulombMatrix|coulomb matrix eig 174', 'BondFractions|Mo - Np bond frac.', 'BondFractions|Cr - Te bond frac.', 'BondFractions|Pt - Sc bond frac.', 'BondFractions|Ru - Yb bond frac.', 'BondFractions|Au - P bond frac.', 'BondFractions|Er - Tb bond frac.', 'BondFractions|Ge - Ti bond frac.', 'BondFractions|Th - Zn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 220', 'BondFractions|F - Pm bond frac.', 'BondFractions|Ba - Ir bond frac.', 'BondFractions|C - Sn bond frac.', 'BondFractions|Ac - Hg bond frac.', 'BondFractions|Cs - Dy bond frac.', 'BondFractions|Co - Sm bond frac.', 'BondFractions|As - Na bond frac.', 'BondFractions|H - Na bond frac.', 'BondFractions|N - Pb bond frac.', 'CoulombMatrix|coulomb matrix eig 131', 'BondFractions|Sr - Tb bond frac.', 'BondFractions|Li - Tb bond frac.', 'CoulombMatrix|coulomb matrix eig 246', 'BondFractions|Au - Gd bond frac.', 'BondFractions|Sc - Tl bond frac.', 'BondFractions|Mo - Si bond frac.', 'BondFractions|Ba - Pt bond frac.', 'BondFractions|Mo - Na bond frac.', 'CoulombMatrix|coulomb matrix eig 215', 'XRDPowderPattern|xrd_2', 'BondFractions|Cu - Ga bond frac.', 'CoulombMatrix|coulomb matrix eig 39', 'SineCoulombMatrix|sine coulomb matrix eig 188', 'SineCoulombMatrix|sine coulomb matrix eig 25', 'SineCoulombMatrix|sine coulomb matrix eig 224', 'BondFractions|Ba - Bi bond frac.', 'BondFractions|Np - Ru bond frac.', 'BondFractions|Ir - Y bond frac.', 'BondFractions|Ag - Lu bond frac.', 'BondFractions|Ho - Pr bond frac.', 'BondFractions|Cr - Dy bond frac.', 'BondFractions|Bi - Lu bond frac.', 'BondFractions|Ce - Pt bond frac.', 'BondFractions|Dy - Sc bond frac.', 'BondFractions|K - Rh bond frac.', 'BondFractions|Sn - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 70', 'BondFractions|Ga - Re bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 264', 'BondFractions|Sb - Y bond frac.', 'BondFractions|Co - Re bond frac.', 'BondFractions|P - Ta bond frac.', 'BondFractions|Ir - Ru bond frac.', 'BondFractions|Lu - Se bond frac.', 'BondFractions|Nd - P bond frac.', 'BondFractions|Fe - Rh bond frac.', 'BondFractions|Cs - Eu bond frac.', 'BondFractions|Np - Te bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 214', 'CoulombMatrix|coulomb matrix eig 151', 'BondFractions|Bi - Np bond frac.', 'BondFractions|Eu - Li bond frac.', 'BondFractions|Pd - Sn bond frac.', 'BondFractions|Co - I bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 216', 'BondFractions|Ce - W bond frac.', 'BondFractions|Bi - Tc bond frac.', 'BondFractions|Be - Ta bond frac.', 'BondFractions|Cs - K bond frac.', 'BondFractions|Se - Tc bond frac.', 'BondFractions|In - Tl bond frac.', 'CoulombMatrix|coulomb matrix eig 41', 'BondFractions|Nd - Sc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 81', 'BondFractions|Ac - H bond frac.', 'BondFractions|Fe - In bond frac.', 'BondFractions|C - Zr bond frac.', 'BondFractions|Ni - Os bond frac.', 'XRDPowderPattern|xrd_10', 'BondFractions|Al - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 106', 'BondFractions|N - Tc bond frac.', 'BondFractions|P - Sn bond frac.', 'BondFractions|Ho - Rb bond frac.', 'BondFractions|Pb - Pb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 74', 'BondFractions|Ho - O bond frac.', 'BondFractions|La - Pd bond frac.', 'BondFractions|Nd - Tb bond frac.', 'BondFractions|Al - Y bond frac.', 'BondFractions|F - Pt bond frac.', 'BondFractions|Os - Pt bond frac.', 'BondFractions|Hg - Sr bond frac.', 'BondFractions|Fe - Ta bond frac.', 'BondFractions|Ce - Sn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 54', 'BondFractions|F - In bond frac.', 'BondFractions|Ag - Bi bond frac.', 'CoulombMatrix|coulomb matrix eig 269', 'BondFractions|Sm - Zr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 288', 'BondFractions|Nb - Ni bond frac.', 'CoulombMatrix|coulomb matrix eig 165', 'BondFractions|Ta - Th bond frac.', 'BondFractions|Rb - Yb bond frac.', 'BondFractions|Lu - Tm bond frac.', 'BondFractions|Ca - Pm bond frac.', 'BondFractions|I - I bond frac.', 'BondFractions|Pa - Y bond frac.', 'BondFractions|In - Zr bond frac.', 'BondFractions|Mn - Tb bond frac.', 'BondFractions|H - Sb bond frac.', 'BondFractions|S - Ta bond frac.', 'BondFractions|Ac - La bond frac.', 'BondFractions|Mn - Se bond frac.', 'BondFractions|Re - Ti bond frac.', 'BondFractions|Pb - Zr bond frac.', 'BondFractions|Au - V bond frac.', 'BondFractions|Np - Rb bond frac.', 'BondFractions|Se - Si bond frac.', 'BondFractions|Rb - Te bond frac.', 'BondFractions|Au - Cl bond frac.', 'BondFractions|Ir - Rh bond frac.', 'BondFractions|Al - H bond frac.', 'BondFractions|Cu - La bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 274', 'BondFractions|Hf - V bond frac.', 'BondFractions|S - Tl bond frac.', 'BondFractions|Cl - In bond frac.', 'BondFractions|C - Cd bond frac.', 'BondFractions|Pb - V bond frac.', 'BondFractions|Na - Pt bond frac.', 'BondFractions|Ag - Xe bond frac.', 'BondFractions|Sr - W bond frac.', 'BondFractions|Au - Tc bond frac.', 'BondFractions|I - Pr bond frac.', 'BondFractions|As - Th bond frac.', 'BondFractions|Tc - Tl bond frac.', 'CoulombMatrix|coulomb matrix eig 204', 'SineCoulombMatrix|sine coulomb matrix eig 271', 'XRDPowderPattern|xrd_88', 'BondFractions|Eu - P bond frac.', 'BondFractions|Er - Eu bond frac.', 'BondFractions|H - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 120', 'BondFractions|Rh - U bond frac.', 'BondFractions|Li - Se bond frac.', 'BondFractions|Be - Hg bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 79', 'BondFractions|Gd - Pt bond frac.', 'BondFractions|Er - Mn bond frac.', 'CoulombMatrix|coulomb matrix eig 50', 'BondFractions|Bi - Rh bond frac.', 'BondFractions|Cl - Cs bond frac.', 'CoulombMatrix|coulomb matrix eig 35', 'BondFractions|Tm - Y bond frac.', 'CoulombMatrix|coulomb matrix eig 279', 'CoulombMatrix|coulomb matrix eig 292', 'BondFractions|Hf - Se bond frac.', 'BondFractions|Te - Te bond frac.', 'BondFractions|Gd - Sm bond frac.', 'BondFractions|Tl - V bond frac.', 'BondFractions|K - Sb bond frac.', 'BondFractions|N - Rb bond frac.', 'BondFractions|Pt - W bond frac.', 'BondFractions|Au - Tb bond frac.', 'BondFractions|Ge - H bond frac.', 'BondFractions|Kr - Sb bond frac.', 'BondFractions|Be - Ge bond frac.', 'BondFractions|Ni - Se bond frac.', 'BondFractions|B - Th bond frac.', 'BondFractions|Bi - Pb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 34', 'BondFractions|Eu - Nb bond frac.', 'BondFractions|Pr - Te bond frac.', 'BondFractions|C - Ge bond frac.', 'BondFractions|Ge - Yb bond frac.', 'BondFractions|I - Pm bond frac.', 'BondFractions|Nd - Sn bond frac.', 'BondFractions|Se - Th bond frac.', 'BondFractions|Gd - Sr bond frac.', 'BondFractions|Ge - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 286', 'BondFractions|Cd - Ni bond frac.', 'BondFractions|Al - Ga bond frac.', 'XRDPowderPattern|xrd_34', 'BondFractions|In - La bond frac.', 'BondFractions|Cl - Pm bond frac.', 'BondFractions|Ba - Fe bond frac.', 'BondFractions|Np - Tm bond frac.', 'BondFractions|Eu - V bond frac.', 'BondFractions|Ba - F bond frac.', 'BondFractions|Ba - Mo bond frac.', 'BondFractions|Na - Tc bond frac.', 'BondFractions|Se - Sn bond frac.', 'CoulombMatrix|coulomb matrix eig 181', 'BondFractions|Cr - Re bond frac.', 'BondFractions|Hf - Tm bond frac.', 'BondFractions|Er - Pm bond frac.', 'BondFractions|Lu - Sc bond frac.', 'BondFractions|Au - Dy bond frac.', 'BondFractions|Sm - Sn bond frac.', 'BondFractions|Ag - Mg bond frac.', 'BondFractions|In - Se bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 254', 'BondFractions|Y - Y bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 27', 'SineCoulombMatrix|sine coulomb matrix eig 30', 'BondFractions|Pt - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 72', 'BondFractions|Ag - Fe bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 215', 'XRDPowderPattern|xrd_104', 'BondFractions|Ce - Mn bond frac.', 'XRDPowderPattern|xrd_26', 'BondFractions|H - Nb bond frac.', 'CoulombMatrix|coulomb matrix eig 243', 'BondFractions|Cs - P bond frac.', 'BondFractions|Br - Tb bond frac.', 'BondFractions|Cs - N bond frac.', 'BondFractions|Nb - Y bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 290', 'BondFractions|Fe - Ru bond frac.', 'BondFractions|Br - Cu bond frac.', 'BondFractions|As - Ho bond frac.', 'BondFractions|In - Ni bond frac.', 'BondFractions|Br - Lu bond frac.', 'BondFractions|Ir - Sn bond frac.', 'BondFractions|Hg - S bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 59', 'BondFractions|Ir - Ir bond frac.', 'BondFractions|Ba - Ho bond frac.', 'BondFractions|Br - Zr bond frac.', 'CoulombMatrix|coulomb matrix eig 105', 'BondFractions|Cl - Te bond frac.', 'BondFractions|I - W bond frac.', 'BondFractions|Sm - Ta bond frac.', 'BondFractions|Hf - Tc bond frac.', 'BondFractions|Li - Pr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 159', 'BondFractions|U - Y bond frac.', 'BondFractions|Ce - Er bond frac.', 'BondFractions|Nd - Pr bond frac.', 'BondFractions|Pd - Sc bond frac.', 'BondFractions|Ba - Pu bond frac.', 'BondFractions|Rh - V bond frac.', 'BondFractions|Cr - Mo bond frac.', 'BondFractions|H - W bond frac.', 'BondFractions|Eu - Tl bond frac.', 'BondFractions|Br - Ir bond frac.', 'CoulombMatrix|coulomb matrix eig 186', 'BondFractions|F - Se bond frac.', 'BondFractions|Eu - Tm bond frac.', 'BondFractions|Ni - Zr bond frac.', 'BondFractions|I - P bond frac.', 'XRDPowderPattern|xrd_102', 'CoulombMatrix|coulomb matrix eig 178', 'BondFractions|Fe - Sm bond frac.', 'BondFractions|F - Nb bond frac.', 'BondFractions|Ag - Pm bond frac.', 'BondFractions|O - Rb bond frac.', 'BondFractions|Nd - Se bond frac.', 'BondFractions|Rb - Tc bond frac.', 'BondFractions|Bi - Bi bond frac.', 'BondFractions|Ca - Pd bond frac.', 'BondFractions|Cu - Sr bond frac.', 'BondFractions|F - Hf bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 66', 'BondFractions|Ir - Pm bond frac.', 'CoulombMatrix|coulomb matrix eig 233', 'BondFractions|Pt - Ta bond frac.', 'XRDPowderPattern|xrd_108', 'BondFractions|Gd - Re bond frac.', 'BondFractions|Si - U bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 285', 'BondFractions|Sn - Zn bond frac.', 'BondFractions|Cs - Re bond frac.', 'BondFractions|Fe - Nd bond frac.', 'BondFractions|Sb - V bond frac.', 'BondFractions|Fe - Gd bond frac.', 'BondFractions|Cl - Dy bond frac.', 'BondFractions|Al - Tc bond frac.', 'BondFractions|Ho - Pa bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 141', 'SineCoulombMatrix|sine coulomb matrix eig 95', 'BondFractions|Pm - Pu bond frac.', 'CoulombMatrix|coulomb matrix eig 210', 'BondFractions|Bi - Yb bond frac.', 'BondFractions|Mo - Ta bond frac.', 'BondFractions|Be - Ce bond frac.', 'BondFractions|F - Ge bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 294', 'BondFractions|Br - Br bond frac.', 'BondFractions|C - Pd bond frac.', 'BondFractions|Ir - La bond frac.', 'BondFractions|Pd - Se bond frac.', 'BondFractions|Li - Pb bond frac.', 'BondFractions|Br - Sc bond frac.', 'BondFractions|Lu - Zr bond frac.', 'BondFractions|Ag - H bond frac.', 'CoulombMatrix|coulomb matrix eig 76', 'BondFractions|Ac - Ru bond frac.', 'BondFractions|Hf - Ni bond frac.', 'BondFractions|S - Xe bond frac.', 'BondFractions|Li - Zr bond frac.', 'BondFractions|Br - Dy bond frac.', 'BondFractions|Ir - Sb bond frac.', 'BondFractions|Nd - Ru bond frac.', 'BondFractions|Ho - Pm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 126', 'SineCoulombMatrix|sine coulomb matrix eig 110', 'BondFractions|K - Np bond frac.', 'BondFractions|La - Th bond frac.', 'BondFractions|Cs - Zr bond frac.', 'BondFractions|I - Ru bond frac.', 'BondFractions|Au - La bond frac.', 'BondFractions|B - Ho bond frac.', 'XRDPowderPattern|xrd_90', 'BondFractions|Os - Tm bond frac.', 'BondFractions|Ru - Sr bond frac.', 'BondFractions|Cd - Ir bond frac.', 'BondFractions|Os - Sc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 201', 'BondFractions|Gd - Nd bond frac.', 'BondFractions|Ir - Mo bond frac.', 'BondFractions|Ba - Mg bond frac.', 'BondFractions|Hf - Nb bond frac.', 'BondFractions|Ge - Pm bond frac.', 'BondFractions|Cl - La bond frac.', 'BondFractions|As - Co bond frac.', 'BondFractions|Pu - S bond frac.', 'BondFractions|As - Se bond frac.', 'BondFractions|Na - Pu bond frac.', 'BondFractions|B - Dy bond frac.', 'BondFractions|H - Sm bond frac.', 'BondFractions|Co - Hf bond frac.', 'BondFractions|K - Te bond frac.', 'BondFractions|Nb - Ti bond frac.', 'BondFractions|Fe - Tc bond frac.', 'BondFractions|Ho - Li bond frac.', 'BondFractions|La - Na bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 178', 'BondFractions|Tc - U bond frac.', 'BondFractions|Nd - Rb bond frac.', 'BondFractions|Bi - Ti bond frac.', 'BondFractions|Ge - Np bond frac.', 'BondFractions|Er - Tc bond frac.', 'BondFractions|Nd - S bond frac.', 'BondFractions|Br - Pt bond frac.', 'BondFractions|Hg - Nb bond frac.', 'CoulombMatrix|coulomb matrix eig 28', 'BondFractions|Pd - Tm bond frac.', 'BondFractions|Pb - Ti bond frac.', 'BondFractions|Nd - Pa bond frac.', 'BondFractions|Ac - Se bond frac.', 'BondFractions|B - Ga bond frac.', 'BondFractions|Dy - N bond frac.', 'BondFractions|As - Er bond frac.', 'BondFractions|Np - V bond frac.', 'BondFractions|Lu - U bond frac.', 'BondFractions|Ge - Ho bond frac.', 'BondFractions|Br - Pd bond frac.', 'BondFractions|In - Pm bond frac.', 'BondFractions|Ca - Ga bond frac.', 'BondFractions|Ca - Xe bond frac.', 'BondFractions|Re - Te bond frac.', 'BondFractions|Au - B bond frac.', 'BondFractions|Er - Pu bond frac.', 'BondFractions|Np - Pa bond frac.', 'BondFractions|Ac - Pr bond frac.', 'BondFractions|Li - Te bond frac.', 'BondFractions|Ce - Fe bond frac.', 'BondFractions|Sr - U bond frac.', 'BondFractions|Al - U bond frac.', 'BondFractions|Pd - W bond frac.', 'BondFractions|F - Os bond frac.', 'BondFractions|Ac - Al bond frac.', 'CoulombMatrix|coulomb matrix eig 276', 'BondFractions|Cu - Eu bond frac.', 'BondFractions|Ta - Zn bond frac.', 'BondFractions|Cu - Ru bond frac.', 'BondFractions|Be - Dy bond frac.', 'XRDPowderPattern|xrd_110', 'BondFractions|La - Pm bond frac.', 'BondFractions|Bi - Mn bond frac.', 'BondFractions|Eu - Sc bond frac.', 'BondFractions|Ba - Co bond frac.', 'BondFractions|Sm - Sr bond frac.', 'BondFractions|Hg - N bond frac.', 'BondFractions|F - Pu bond frac.', 'BondFractions|As - Si bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 131', 'BondFractions|Cr - In bond frac.', 'CoulombMatrix|coulomb matrix eig 15', 'BondFractions|S - Zr bond frac.', 'BondFractions|Ge - Tm bond frac.', 'BondFractions|Ho - Nd bond frac.', 'BondFractions|B - Ba bond frac.', 'BondFractions|B - Hg bond frac.', 'BondFractions|Ag - Si bond frac.', 'BondFractions|H - H bond frac.', 'BondFractions|Ca - Ge bond frac.', 'BondFractions|Au - Np bond frac.', 'BondFractions|Pd - U bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 164', 'BondFractions|Cs - I bond frac.', 'BondFractions|Se - Zr bond frac.', 'BondFractions|Be - Tb bond frac.', 'CoulombMatrix|coulomb matrix eig 72', 'BondFractions|Hf - Sn bond frac.', 'BondFractions|I - Rh bond frac.', 'BondFractions|B - Br bond frac.', 'BondFractions|N - Sn bond frac.', 'BondFractions|Cu - Nd bond frac.', 'BondFractions|Na - Y bond frac.', 'BondFractions|Ir - Tb bond frac.', 'BondFractions|Nb - Os bond frac.', 'BondFractions|Pu - Zn bond frac.', 'BondFractions|Cl - Rb bond frac.', 'BondFractions|Pu - Sc bond frac.', 'BondFractions|Ca - Tm bond frac.', 'CoulombMatrix|coulomb matrix eig 21', 'BondFractions|Eu - Pt bond frac.', 'BondFractions|Er - Np bond frac.', 'BondFractions|B - Pb bond frac.', 'BondFractions|Pb - Rh bond frac.', 'BondFractions|Pu - Si bond frac.', 'BondFractions|Hg - P bond frac.', 'BondFractions|Dy - Sb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 279', 'BondFractions|Pa - Si bond frac.', 'BondFractions|Er - N bond frac.', 'BondFractions|Pr - Sm bond frac.', 'BondFractions|Cs - H bond frac.', 'BondFractions|Ce - Pa bond frac.', 'CoulombMatrix|coulomb matrix eig 112', 'BondFractions|Nb - O bond frac.', 'BondFractions|Cl - Tc bond frac.', 'BondFractions|V - Zr bond frac.', 'BondFractions|Rh - Ru bond frac.', 'BondFractions|Er - Mo bond frac.', 'BondFractions|Na - U bond frac.', 'BondFractions|Os - Pm bond frac.', 'BondFractions|Ce - Sc bond frac.', 'BondFractions|Sr - Yb bond frac.', 'BondFractions|P - Pt bond frac.', 'BondFractions|Rb - Tl bond frac.', 'BondFractions|La - Zr bond frac.', 'BondFractions|Si - Sm bond frac.', 'BondFractions|Al - Pd bond frac.', 'BondFractions|Cd - Np bond frac.', 'BondFractions|Mo - Sb bond frac.', 'XRDPowderPattern|xrd_32', 'BondFractions|Ru - Sn bond frac.', 'BondFractions|Hg - Rb bond frac.', 'BondFractions|Rb - Ru bond frac.', 'XRDPowderPattern|xrd_20', 'BondFractions|Ga - Hg bond frac.', 'BondFractions|Hg - Os bond frac.', 'BondFractions|Br - Er bond frac.', 'BondFractions|Re - Re bond frac.', 'BondFractions|H - Mo bond frac.', 'BondFractions|Cd - K bond frac.', 'BondFractions|Ag - Dy bond frac.', 'BondFractions|C - Ir bond frac.', 'BondFractions|Nd - Nd bond frac.', 'BondFractions|Ag - B bond frac.', 'BondFractions|Sm - V bond frac.', 'BondFractions|K - Xe bond frac.', 'BondFractions|I - Os bond frac.', 'BondFractions|Na - Rh bond frac.', 'CoulombMatrix|coulomb matrix eig 273', 'BondFractions|Pm - Pt bond frac.', 'CoulombMatrix|coulomb matrix eig 148', 'BondFractions|Al - Ge bond frac.', 'BondFractions|Ag - Ni bond frac.', 'BondFractions|Ag - Mo bond frac.', 'BondFractions|Hg - O bond frac.', 'BondFractions|Mn - Pr bond frac.', 'BondFractions|O - Tb bond frac.', 'BondFractions|Ag - Nd bond frac.', 'CoulombMatrix|coulomb matrix eig 46', 'BondFractions|K - Re bond frac.', 'BondFractions|Dy - Ta bond frac.', 'BondFractions|Mn - Nb bond frac.', 'BondFractions|Tb - Zn bond frac.', 'BondFractions|Nd - Xe bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 98', 'BondFractions|Ca - Yb bond frac.', 'BondFractions|S - Sr bond frac.', 'BondFractions|Au - Au bond frac.', 'BondFractions|C - Tl bond frac.', 'BondFractions|Si - Tl bond frac.', 'BondFractions|Ge - Pr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 155', 'SineCoulombMatrix|sine coulomb matrix eig 218', 'SineCoulombMatrix|sine coulomb matrix eig 15', 'BondFractions|Ba - Na bond frac.', 'BondFractions|Eu - La bond frac.', 'BondFractions|K - Y bond frac.', 'BondFractions|Cr - W bond frac.', 'BondFractions|Bi - Y bond frac.', 'BondFractions|Ba - Ta bond frac.', 'BondFractions|H - Mg bond frac.', 'BondFractions|Ag - Au bond frac.', 'BondFractions|C - Sr bond frac.', 'BondFractions|Au - Ho bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 256', 'BondFractions|Os - Sn bond frac.', 'BondFractions|Rb - Y bond frac.', 'BondFractions|Ti - Y bond frac.', 'BondFractions|Be - Ho bond frac.', 'BondFractions|Pr - Sb bond frac.', 'BondFractions|I - O bond frac.', 'BondFractions|Hf - Pd bond frac.', 'BondFractions|Ba - Si bond frac.', 'BondFractions|C - U bond frac.', 'BondFractions|Ge - Xe bond frac.', 'BondFractions|Pt - Rh bond frac.', 'CoulombMatrix|coulomb matrix eig 61', 'BondFractions|Co - Ge bond frac.', 'BondFractions|Ho - K bond frac.', 'BondFractions|Sc - Tc bond frac.', 'BondFractions|Pd - Pt bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 149', 'BondFractions|Dy - In bond frac.', 'BondFractions|Ce - V bond frac.', 'BondFractions|Ga - Rh bond frac.', 'BondFractions|Hf - Si bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 228', 'BondFractions|Lu - Pt bond frac.', 'BondFractions|Pt - Se bond frac.', 'BondFractions|Cr - Y bond frac.', 'BondFractions|Ni - Tc bond frac.', 'BondFractions|Sb - Xe bond frac.', 'BondFractions|Pd - S bond frac.', 'BondFractions|Ba - Pd bond frac.', 'BondFractions|As - Ge bond frac.', 'BondFractions|Rh - Tb bond frac.', 'BondFractions|Pr - Sn bond frac.', 'BondFractions|Ga - Ru bond frac.', 'BondFractions|Sb - Sc bond frac.', 'BondFractions|Ag - P bond frac.', 'CoulombMatrix|coulomb matrix eig 121', 'BondFractions|Cs - Pb bond frac.', 'BondFractions|Ti - Tm bond frac.', 'BondFractions|As - V bond frac.', 'CoulombMatrix|coulomb matrix eig 191', 'BondFractions|Mg - Pu bond frac.', 'BondFractions|Sr - Xe bond frac.', 'BondFractions|Ba - Sm bond frac.', 'BondFractions|Ag - Cu bond frac.', 'BondFractions|As - Tc bond frac.', 'BondFractions|Er - Ge bond frac.', 'BondFractions|Sc - W bond frac.', 'BondFractions|Ni - Ru bond frac.', 'BondFractions|As - Bi bond frac.', 'BondFractions|C - Re bond frac.', 'BondFractions|Np - Sb bond frac.', 'BondFractions|Ca - Zr bond frac.', 'BondFractions|Br - Zn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 127', 'BondFractions|Tl - Zn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 168', 'BondFractions|Np - Os bond frac.', 'BondFractions|Ta - Ti bond frac.', 'BondFractions|Ag - Ag bond frac.', 'BondFractions|Gd - Gd bond frac.', 'XRDPowderPattern|xrd_96', 'BondFractions|Pa - Yb bond frac.', 'BondFractions|Nb - V bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 139', 'BondFractions|Ir - Ti bond frac.', 'BondFractions|Hf - S bond frac.', 'BondFractions|Tb - W bond frac.', 'BondFractions|Gd - V bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 151', 'BondFractions|Cd - Cl bond frac.', 'BondFractions|Ac - Ac bond frac.', 'BondFractions|Cu - Er bond frac.', 'BondFractions|Pt - Yb bond frac.', 'BondFractions|Ag - Ti bond frac.', 'CoulombMatrix|coulomb matrix eig 285', 'BondFractions|Lu - Pr bond frac.', 'BondFractions|As - Cu bond frac.', 'CoulombMatrix|coulomb matrix eig 193', 'BondFractions|Sm - Tc bond frac.', 'BondFractions|Ba - Xe bond frac.', 'BondFractions|Cu - Sb bond frac.', 'BondFractions|Fe - Pd bond frac.', 'BondFractions|Rb - Ti bond frac.', 'BondFractions|Cd - Pa bond frac.', 'BondFractions|Cu - Pd bond frac.', 'BondFractions|Mg - Pa bond frac.', 'BondFractions|Er - Ir bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 181', 'BondFractions|He - Si bond frac.', 'BondFractions|Ce - Y bond frac.', 'BondFractions|Ag - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 66', 'CoulombMatrix|coulomb matrix eig 294', 'BondFractions|Hf - W bond frac.', 'BondFractions|Pd - Ti bond frac.', 'BondFractions|Cr - Ho bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 283', 'BondFractions|Ce - Cs bond frac.', 'BondFractions|Ca - Dy bond frac.', 'CoulombMatrix|coulomb matrix eig 114', 'BondFractions|Bi - Cu bond frac.', 'BondFractions|Ag - Ca bond frac.', 'BondFractions|Si - Tm bond frac.', 'BondFractions|H - Ir bond frac.', 'BondFractions|Pt - U bond frac.', 'XRDPowderPattern|xrd_84', 'BondFractions|Au - Sc bond frac.', 'BondFractions|Bi - Hg bond frac.', 'BondFractions|Ir - Rb bond frac.', 'BondFractions|Bi - C bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 258', 'BondFractions|Rb - U bond frac.', 'BondFractions|Er - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 82', 'BondFractions|I - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 226', 'BondFractions|Cs - Pr bond frac.', 'CoulombMatrix|coulomb matrix eig 248', 'SineCoulombMatrix|sine coulomb matrix eig 248', 'BondFractions|Mo - Zr bond frac.', 'BondFractions|Au - Mg bond frac.', 'BondFractions|Ca - Tc bond frac.', 'CoulombMatrix|coulomb matrix eig 266', 'BondFractions|Ag - Pr bond frac.', 'BondFractions|S - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 128', 'BondFractions|Cs - Ru bond frac.', 'BondFractions|Au - Os bond frac.', 'BondFractions|Br - Cs bond frac.', 'BondFractions|Lu - Zn bond frac.', 'BondFractions|Be - Sb bond frac.', 'BondFractions|Bi - Br bond frac.', 'BondFractions|Cd - Si bond frac.', 'BondFractions|La - Os bond frac.', 'BondFractions|Li - Nb bond frac.', 'BondFractions|I - Zn bond frac.', 'BondFractions|Y - Zr bond frac.', 'BondFractions|B - Eu bond frac.', 'BondFractions|Pd - Sb bond frac.', 'BondFractions|Eu - Zr bond frac.', 'BondFractions|Rh - Si bond frac.', 'BondFractions|Cl - Pd bond frac.', 'BondFractions|Dy - Ir bond frac.', 'BondFractions|B - Ce bond frac.', 'BondFractions|Cs - Er bond frac.', 'BondFractions|Ni - Xe bond frac.', 'CoulombMatrix|coulomb matrix eig 284', 'BondFractions|N - U bond frac.', 'BondFractions|Be - Tl bond frac.', 'BondFractions|Bi - N bond frac.', 'BondFractions|Fe - Re bond frac.', 'BondFractions|Os - Pd bond frac.', 'BondFractions|P - Tm bond frac.', 'BondFractions|Rh - Rh bond frac.', 'BondFractions|Mn - Re bond frac.', 'BondFractions|Ho - Tl bond frac.', 'BondFractions|O - Zr bond frac.', 'BondFractions|Ru - Y bond frac.', 'BondFractions|Mo - Sr bond frac.', 'BondFractions|Pt - Xe bond frac.', 'BondFractions|Lu - O bond frac.', 'BondFractions|Ho - Sr bond frac.', 'BondFractions|Pa - Sm bond frac.', 'BondFractions|Be - Er bond frac.', 'BondFractions|Pm - Te bond frac.', 'BondFractions|Cl - Hg bond frac.', 'BondFractions|Hg - Rh bond frac.', 'BondFractions|Pd - Xe bond frac.', 'CoulombMatrix|coulomb matrix eig 49', 'CoulombMatrix|coulomb matrix eig 153', 'BondFractions|Cl - Pr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 134', 'BondFractions|N - Ru bond frac.', 'BondFractions|Sb - Yb bond frac.', 'BondFractions|I - Np bond frac.', 'BondFractions|Ba - I bond frac.', 'BondFractions|B - Cs bond frac.', 'BondFractions|Pd - Tl bond frac.', 'BondFractions|Nd - Pt bond frac.', 'BondFractions|Au - Ru bond frac.', 'BondFractions|Na - Tl bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 251', 'BondFractions|Se - Tl bond frac.', 'BondFractions|Au - Na bond frac.', 'BondFractions|P - Zr bond frac.', 'BondFractions|Eu - Mg bond frac.', 'BondFractions|Bi - Te bond frac.', 'BondFractions|Lu - Na bond frac.', 'BondFractions|Ag - Yb bond frac.', 'CoulombMatrix|coulomb matrix eig 16', 'BondFractions|Hf - P bond frac.', 'BondFractions|In - Rb bond frac.', 'CoulombMatrix|coulomb matrix eig 240', 'BondFractions|Xe - Xe bond frac.', 'BondFractions|H - Ni bond frac.', 'BondFractions|F - La bond frac.', 'BondFractions|Pm - Si bond frac.', 'BondFractions|C - Ce bond frac.', 'BondFractions|Ca - Er bond frac.', 'BondFractions|Ge - Tl bond frac.', 'BondFractions|Tm - Zn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 276', 'BondFractions|Ni - Sn bond frac.', 'BondFractions|Be - Ga bond frac.', 'BondFractions|Cr - Os bond frac.', 'BondFractions|Ho - N bond frac.', 'BondFractions|In - N bond frac.', 'BondFractions|Lu - Os bond frac.', 'CoulombMatrix|coulomb matrix eig 11', 'BondFractions|As - Gd bond frac.', 'CoulombMatrix|coulomb matrix eig 48', 'BondFractions|Ho - V bond frac.', 'BondFractions|O - U bond frac.', 'BondFractions|P - Se bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 28', 'BondFractions|Cr - Rh bond frac.', 'BondFractions|Tl - Tl bond frac.', 'BondFractions|O - Ta bond frac.', 'BondFractions|Cs - Sb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 272', 'BondFractions|F - Th bond frac.', 'BondFractions|Sm - W bond frac.', 'BondFractions|Pd - Pr bond frac.', 'BondFractions|Dy - Er bond frac.', 'BondFractions|Os - Zr bond frac.', 'BondFractions|Cs - Ti bond frac.', 'BondFractions|Cd - Se bond frac.', 'BondFractions|F - U bond frac.', 'BondFractions|Mn - Pd bond frac.', 'BondFractions|Ni - U bond frac.', 'BondFractions|Cu - Rh bond frac.', 'BondFractions|Pd - Tb bond frac.', 'CoulombMatrix|coulomb matrix eig 169', 'BondFractions|S - Th bond frac.', 'BondFractions|H - Zn bond frac.', 'BondFractions|Cs - F bond frac.', 'BondFractions|Dy - Ga bond frac.', 'BondFractions|Co - Pt bond frac.', 'BondFractions|As - Li bond frac.', 'BondFractions|Ba - Pb bond frac.', 'BondFractions|Os - Tb bond frac.', 'BondFractions|Au - Er bond frac.', 'BondFractions|Eu - Ta bond frac.', 'CoulombMatrix|coulomb matrix eig 177', 'BondFractions|Cs - Mn bond frac.', 'BondFractions|Pr - Si bond frac.', 'BondFractions|Ru - Zn bond frac.', 'BondFractions|Ni - Pa bond frac.', 'BondFractions|Co - W bond frac.', 'CoulombMatrix|coulomb matrix eig 212', 'BondFractions|Co - In bond frac.', 'BondFractions|Au - Mn bond frac.', 'BondFractions|Cl - Sb bond frac.', 'BondFractions|O - Te bond frac.', 'BondFractions|W - W bond frac.', 'BondFractions|Ga - Na bond frac.', 'BondFractions|Ho - Lu bond frac.', 'BondFractions|Co - La bond frac.', 'BondFractions|Ce - Li bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 195', 'BondFractions|Sn - Tm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 33', 'BondFractions|Pb - Te bond frac.', 'BondFractions|Be - Zr bond frac.', 'BondFractions|Eu - Sr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 266', 'BondFractions|Pm - Yb bond frac.', 'BondFractions|N - Sm bond frac.', 'BondFractions|Be - Bi bond frac.', 'BondFractions|Li - U bond frac.', 'BondFractions|Cd - Os bond frac.', 'BondFractions|Ni - Sb bond frac.', 'BondFractions|Pa - Tc bond frac.', 'CoulombMatrix|coulomb matrix eig 173', 'BondFractions|Ge - Ru bond frac.', 'BondFractions|Gd - Li bond frac.', 'BondFractions|Ba - Ba bond frac.', 'BondFractions|Au - O bond frac.', 'BondFractions|Ca - Cd bond frac.', 'CoulombMatrix|coulomb matrix eig 77', 'BondFractions|Ta - Tc bond frac.', 'BondFractions|Nd - Os bond frac.', 'BondFractions|Ge - Pa bond frac.', 'BondFractions|H - Tm bond frac.', 'BondFractions|Al - Ho bond frac.', 'CoulombMatrix|coulomb matrix eig 67', 'BondFractions|Nd - Y bond frac.', 'BondFractions|Er - S bond frac.', 'BondFractions|Pr - V bond frac.', 'CoulombMatrix|coulomb matrix eig 190', 'BondFractions|K - Ru bond frac.', 'BondFractions|La - Pb bond frac.', 'CoulombMatrix|coulomb matrix eig 22', 'XRDPowderPattern|xrd_106', 'BondFractions|Cd - Pr bond frac.', 'BondFractions|Pb - Tl bond frac.', 'CoulombMatrix|coulomb matrix eig 52', 'BondFractions|Co - Ga bond frac.', 'BondFractions|Mg - Xe bond frac.', 'BondFractions|Ge - Nb bond frac.', 'BondFractions|Zn - Zr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 109', 'BondFractions|Br - F bond frac.', 'BondFractions|Ga - Pm bond frac.', 'BondFractions|Pa - Te bond frac.', 'BondFractions|K - Tl bond frac.', 'BondFractions|Rh - Tl bond frac.', 'BondFractions|Ca - Pb bond frac.', 'BondFractions|Ac - Rb bond frac.', 'BondFractions|Gd - Tm bond frac.', 'BondFractions|Gd - Se bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 75', 'BondFractions|Cu - Np bond frac.', 'BondFractions|Au - U bond frac.', 'BondFractions|Cu - H bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 85', 'BondFractions|Np - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 166', 'BondFractions|Li - Sm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 192', 'BondFractions|Pb - U bond frac.', 'BondFractions|Lu - Sb bond frac.', 'BondFractions|Ho - S bond frac.', 'BondFractions|Lu - Ta bond frac.', 'BondFractions|Te - U bond frac.', 'BondFractions|Eu - Hg bond frac.', 'BondFractions|Co - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 85', 'BondFractions|P - Te bond frac.', 'BondFractions|Cs - Mo bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 284', 'BondFractions|N - Tb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 96', 'BondFractions|Ni - Ta bond frac.', 'CoulombMatrix|coulomb matrix eig 64', 'BondFractions|Pb - Se bond frac.', 'BondFractions|H - Si bond frac.', 'BondFractions|I - Te bond frac.', 'BondFractions|Li - W bond frac.', 'BondFractions|Be - Ru bond frac.', 'BondFractions|La - Ni bond frac.', 'BondFractions|As - Nb bond frac.', 'BondFractions|Bi - Gd bond frac.', 'BondFractions|Bi - Ir bond frac.', 'BondFractions|I - Ti bond frac.', 'BondFractions|Eu - Gd bond frac.', 'BondFractions|Hf - Zr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 158', 'SineCoulombMatrix|sine coulomb matrix eig 136', 'BondFractions|La - Sn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 184', 'BondFractions|Sm - Tm bond frac.', 'BondFractions|Mn - Sb bond frac.', 'BondFractions|Er - Hf bond frac.', 'BondFractions|Tc - Zn bond frac.', 'BondFractions|Dy - Re bond frac.', 'BondFractions|Cd - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 142', 'BondFractions|Ba - K bond frac.', 'CoulombMatrix|coulomb matrix eig 175', 'BondFractions|Bi - Rb bond frac.', 'BondFractions|Nb - Re bond frac.', 'BondFractions|Pr - Zr bond frac.', 'BondFractions|Ca - I bond frac.', 'BondFractions|Mg - Mo bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 87', 'BondFractions|Cd - N bond frac.', 'BondFractions|O - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 21', 'BondFractions|Cl - Ta bond frac.', 'BondFractions|Sn - Sn bond frac.', 'BondFractions|Sc - Y bond frac.', 'BondFractions|Br - U bond frac.', 'BondFractions|Hg - Sc bond frac.', 'BondFractions|Mn - Rh bond frac.', 'BondFractions|Ce - Th bond frac.', 'BondFractions|Ca - Hf bond frac.', 'BondFractions|B - Pd bond frac.', 'BondFractions|Co - Tl bond frac.', 'BondFractions|Ba - Li bond frac.', 'BondFractions|Os - Y bond frac.', 'BondFractions|Tb - Te bond frac.', 'BondFractions|As - Sn bond frac.', 'BondFractions|Cr - Ir bond frac.', 'BondFractions|Hg - Xe bond frac.', 'BondFractions|Cl - Mo bond frac.', 'BondFractions|Cs - Cs bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 121', 'BondFractions|Be - Nd bond frac.', 'BondFractions|Cr - Hg bond frac.', 'BondFractions|Cd - Mo bond frac.', 'BondFractions|Ga - V bond frac.', 'BondFractions|N - Pd bond frac.', 'BondFractions|Au - Nb bond frac.', 'BondFractions|Mo - Os bond frac.', 'BondFractions|Ge - Si bond frac.', 'BondFractions|Tc - Tm bond frac.', 'BondFractions|Dy - V bond frac.', 'BondFractions|Sc - Sn bond frac.', 'BondFractions|Pt - Pt bond frac.', 'BondFractions|La - Sr bond frac.', 'CoulombMatrix|coulomb matrix eig 110', 'BondFractions|Sn - Ta bond frac.', 'BondFractions|Nb - Zn bond frac.', 'CoulombMatrix|coulomb matrix eig 31', 'BondFractions|Nd - Re bond frac.', 'BondFractions|Ho - Nb bond frac.', 'XRDPowderPattern|xrd_118', 'BondFractions|Os - Te bond frac.', 'BondFractions|Lu - Rh bond frac.', 'BondFractions|Cl - Ga bond frac.', 'BondFractions|In - Sr bond frac.', 'CoulombMatrix|coulomb matrix eig 272', 'BondFractions|Cd - P bond frac.', 'BondFractions|Co - Rh bond frac.', 'BondFractions|Cl - Ir bond frac.', 'BondFractions|As - Cs bond frac.', 'BondFractions|Au - Rh bond frac.', 'BondFractions|Ag - Pa bond frac.', 'BondFractions|Ca - Hg bond frac.', 'BondFractions|Ba - Rh bond frac.', 'BondFractions|I - Mn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 37', 'BondFractions|Pu - Te bond frac.', 'BondFractions|Tb - Yb bond frac.', 'CoulombMatrix|coulomb matrix eig 183', 'SineCoulombMatrix|sine coulomb matrix eig 187', 'BondFractions|H - Pd bond frac.', 'BondFractions|Pd - Yb bond frac.', 'BondFractions|Cd - Zr bond frac.', 'BondFractions|Mg - Nd bond frac.', 'BondFractions|Nd - Sr bond frac.', 'BondFractions|La - Pr bond frac.', 'BondFractions|Hf - Tl bond frac.', 'BondFractions|Co - Np bond frac.', 'BondFractions|Ba - Rb bond frac.', 'BondFractions|Tb - Zr bond frac.', 'CoulombMatrix|coulomb matrix eig 143', 'BondFractions|In - Sc bond frac.', 'BondFractions|La - Re bond frac.', 'BondFractions|Gd - K bond frac.', 'BondFractions|Cd - S bond frac.', 'BondFractions|Ce - Zr bond frac.', 'BondFractions|Au - Ni bond frac.', 'BondFractions|Cd - Sb bond frac.', 'BondFractions|K - Pa bond frac.', 'BondFractions|Te - W bond frac.', 'BondFractions|Eu - Rb bond frac.', 'CoulombMatrix|coulomb matrix eig 54', 'BondFractions|Ge - Nd bond frac.', 'BondFractions|C - Pt bond frac.', 'BondFractions|As - Sb bond frac.', 'BondFractions|C - Pu bond frac.', 'BondFractions|Bi - Na bond frac.', 'BondFractions|Ca - Rh bond frac.', 'BondFractions|P - U bond frac.', 'BondFractions|Ca - Sb bond frac.', 'BondFractions|Ir - Tm bond frac.', 'BondFractions|Cu - Pm bond frac.', 'BondFractions|K - Pr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 12', 'BondFractions|Hf - Ti bond frac.', 'CoulombMatrix|coulomb matrix eig 94', 'BondFractions|Pa - Se bond frac.', 'BondFractions|Cr - Sn bond frac.', 'BondFractions|As - Pa bond frac.', 'BondFractions|Cl - Zr bond frac.', 'CoulombMatrix|coulomb matrix eig 98', 'SineCoulombMatrix|sine coulomb matrix eig 65', 'BondFractions|Br - Li bond frac.', 'BondFractions|Co - Pm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 173', 'BondFractions|Ga - U bond frac.', 'BondFractions|Cs - Tl bond frac.', 'BondFractions|As - Mg bond frac.', 'BondFractions|Al - Tl bond frac.', 'BondFractions|K - Nd bond frac.', 'BondFractions|C - Ru bond frac.', 'BondFractions|Ce - Rh bond frac.', 'BondFractions|Br - I bond frac.', 'BondFractions|Gd - Si bond frac.', 'BondFractions|Br - In bond frac.', 'BondFractions|S - U bond frac.', 'XRDPowderPattern|xrd_66', 'BondFractions|Ti - W bond frac.', 'BondFractions|Lu - N bond frac.', 'BondFractions|Au - Cs bond frac.', 'BondFractions|Rb - W bond frac.', 'BondFractions|Br - Fe bond frac.', 'BondFractions|Au - Eu bond frac.', 'BondFractions|Ir - Se bond frac.', 'BondFractions|Be - W bond frac.', 'BondFractions|Hf - Y bond frac.', 'BondFractions|Rb - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 195', 'BondFractions|Cr - I bond frac.', 'BondFractions|Na - Sn bond frac.', 'BondFractions|B - Sr bond frac.', 'BondFractions|Br - Mn bond frac.', 'BondFractions|Cl - Hf bond frac.', 'CoulombMatrix|coulomb matrix eig 146', 'BondFractions|Sb - Sn bond frac.', 'CoulombMatrix|coulomb matrix eig 282', 'BondFractions|N - Tl bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 200', 'BondFractions|Al - Ru bond frac.', 'CoulombMatrix|coulomb matrix eig 30', 'BondFractions|Hf - La bond frac.', 'BondFractions|Al - Pt bond frac.', 'BondFractions|F - Nd bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 44', 'BondFractions|Ga - Ga bond frac.', 'BondFractions|N - Se bond frac.', 'BondFractions|In - Na bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 101', 'BondFractions|Ge - Lu bond frac.', 'BondFractions|Ru - Tb bond frac.', 'BondFractions|Cd - Ta bond frac.', 'BondFractions|Dy - Li bond frac.', 'BondFractions|Os - Si bond frac.', 'BondFractions|As - Zn bond frac.', 'BondFractions|Lu - Rb bond frac.', 'BondFractions|Hg - Th bond frac.', 'BondFractions|Ce - Yb bond frac.', 'CoulombMatrix|coulomb matrix eig 200', 'BondFractions|Cl - Y bond frac.', 'BondFractions|Pt - Sr bond frac.', 'CoulombMatrix|coulomb matrix eig 199', 'BondFractions|Pd - Tc bond frac.', 'BondFractions|Ag - Ga bond frac.', 'BondFractions|Dy - Rb bond frac.', 'BondFractions|Ho - Sc bond frac.', 'BondFractions|V - W bond frac.', 'BondFractions|La - Tb bond frac.', 'BondFractions|C - H bond frac.', 'BondFractions|Br - Os bond frac.', 'BondFractions|In - Li bond frac.', 'BondFractions|Bi - Os bond frac.', 'BondFractions|Bi - Mo bond frac.', 'BondFractions|Mn - Sr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 115', 'BondFractions|Ce - Na bond frac.', 'BondFractions|Sm - Tb bond frac.', 'BondFractions|Br - Ni bond frac.', 'BondFractions|Os - Xe bond frac.', 'BondFractions|H - Rh bond frac.', 'BondFractions|F - Lu bond frac.', 'BondFractions|Co - Tm bond frac.', 'BondFractions|I - Sc bond frac.', 'BondFractions|Rh - W bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 273', 'BondFractions|Hf - Lu bond frac.', 'BondFractions|Ca - Eu bond frac.', 'BondFractions|Ti - Yb bond frac.', 'BondFractions|Cu - Lu bond frac.', 'BondFractions|Er - Ru bond frac.', 'BondFractions|Cu - Y bond frac.', 'BondFractions|Fe - Sb bond frac.', 'BondFractions|Ba - U bond frac.', 'BondFractions|Dy - Hf bond frac.', 'BondFractions|Dy - Sr bond frac.', 'BondFractions|Bi - Ho bond frac.', 'BondFractions|Ba - Re bond frac.', 'BondFractions|Sc - Th bond frac.', 'BondFractions|Bi - Sc bond frac.', 'BondFractions|Hg - Tc bond frac.', 'BondFractions|Sb - Ti bond frac.', 'BondFractions|C - Dy bond frac.', 'BondFractions|La - U bond frac.', 'BondFractions|Cu - Pu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 160', 'SineCoulombMatrix|sine coulomb matrix eig 213', 'BondFractions|Cu - Ho bond frac.', 'BondFractions|Ba - W bond frac.', 'BondFractions|Na - Te bond frac.', 'BondFractions|Br - Mo bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 68', 'BondFractions|Ag - Sr bond frac.', 'BondFractions|Nb - Tc bond frac.', 'BondFractions|Hg - Sn bond frac.', 'BondFractions|Nd - Tl bond frac.', 'BondFractions|K - Ta bond frac.', 'BondFractions|Br - Te bond frac.', 'BondFractions|Er - Er bond frac.', 'BondFractions|La - Se bond frac.', 'BondFractions|Cr - Sb bond frac.', 'BondFractions|Ba - Mn bond frac.', 'BondFractions|In - Ta bond frac.', 'BondFractions|Pb - Ru bond frac.', 'BondFractions|Cr - Rb bond frac.', 'BondFractions|Dy - I bond frac.', 'BondFractions|Pu - Sb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 186', 'BondFractions|Ce - Ti bond frac.', 'BondFractions|Np - Si bond frac.', 'CoulombMatrix|coulomb matrix eig 254', 'BondFractions|Mn - Ru bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 269', 'BondFractions|In - Pa bond frac.', 'BondFractions|Mo - O bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 41', 'BondFractions|Dy - Rh bond frac.', 'BondFractions|Nd - O bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 211', 'BondFractions|Re - Tc bond frac.', 'BondFractions|Er - Sr bond frac.', 'BondFractions|Bi - Li bond frac.', 'BondFractions|Rb - Re bond frac.', 'BondFractions|Cr - Pt bond frac.', 'BondFractions|Cd - Th bond frac.', 'BondFractions|Al - Re bond frac.', 'BondFractions|Ni - Pb bond frac.', 'BondFractions|Au - H bond frac.', 'BondFractions|Ga - Np bond frac.', 'BondFractions|B - Sn bond frac.', 'BondFractions|Eu - Fe bond frac.', 'XRDPowderPattern|xrd_38', 'BondFractions|Mo - Tb bond frac.', 'BondFractions|H - O bond frac.', 'BondFractions|Lu - P bond frac.', 'BondFractions|As - W bond frac.', 'BondFractions|Ag - Li bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 241', 'BondFractions|Nd - V bond frac.', 'CoulombMatrix|coulomb matrix eig 70', 'BondFractions|Cl - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 88', 'BondFractions|P - Pb bond frac.', 'BondFractions|Ho - Th bond frac.', 'BondFractions|La - Mo bond frac.', 'CoulombMatrix|coulomb matrix eig 230', 'BondFractions|Ag - Pb bond frac.', 'BondFractions|Lu - Yb bond frac.', 'BondFractions|Hf - Pr bond frac.', 'BondFractions|Se - Sr bond frac.', 'BondFractions|Co - Hg bond frac.', 'CoulombMatrix|coulomb matrix eig 247', 'SineCoulombMatrix|sine coulomb matrix eig 112', 'XRDPowderPattern|xrd_48', 'BondFractions|Cu - Se bond frac.', 'BondFractions|Ba - Pr bond frac.', 'BondFractions|Ni - Pr bond frac.', 'BondFractions|Cd - Ho bond frac.', 'BondFractions|Pr - U bond frac.', 'BondFractions|Eu - K bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 80', 'CoulombMatrix|coulomb matrix eig 238', 'BondFractions|Nd - Si bond frac.', 'BondFractions|Er - U bond frac.', 'BondFractions|Co - Os bond frac.', 'BondFractions|Ge - La bond frac.', 'CoulombMatrix|coulomb matrix eig 34', 'CoulombMatrix|coulomb matrix eig 208', 'BondFractions|Ge - Rh bond frac.', 'BondFractions|Hg - I bond frac.', 'BondFractions|Gd - W bond frac.', 'BondFractions|Er - Ta bond frac.', 'BondFractions|Re - Y bond frac.', 'BondFractions|Fe - Np bond frac.', 'BondFractions|Ni - Tl bond frac.', 'BondFractions|Gd - Te bond frac.', 'BondFractions|Ru - S bond frac.', 'CoulombMatrix|coulomb matrix eig 263', 'BondFractions|Lu - Mn bond frac.', 'BondFractions|As - Xe bond frac.', 'BondFractions|Ge - N bond frac.', 'BondFractions|Ag - U bond frac.', 'BondFractions|F - Pa bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 18', 'BondFractions|Ni - Sr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 50', 'BondFractions|Ba - Cu bond frac.', 'BondFractions|Rh - Se bond frac.', 'BondFractions|In - Tm bond frac.', 'BondFractions|C - Rb bond frac.', 'BondFractions|F - Mo bond frac.', 'BondFractions|Au - Co bond frac.', 'BondFractions|Ba - Tb bond frac.', 'XRDPowderPattern|xrd_116', 'BondFractions|Br - S bond frac.', 'BondFractions|Ge - Pd bond frac.', 'BondFractions|Nb - Pb bond frac.', 'BondFractions|Be - Re bond frac.', 'BondFractions|Be - Tm bond frac.', 'BondFractions|Cs - Ge bond frac.', 'BondFractions|Gd - Ru bond frac.', 'BondFractions|Li - Ru bond frac.', 'BondFractions|Ca - Tb bond frac.', 'BondFractions|Br - Ti bond frac.', 'BondFractions|Eu - Ru bond frac.', 'BondFractions|Ba - Yb bond frac.', 'BondFractions|As - Sr bond frac.', 'BondFractions|Pa - Zn bond frac.', 'BondFractions|As - In bond frac.', 'BondFractions|Cs - Se bond frac.', 'BondFractions|Pt - Th bond frac.', 'BondFractions|Al - Pu bond frac.', 'BondFractions|Ba - Cr bond frac.', 'BondFractions|Pd - Sr bond frac.', 'BondFractions|Pb - Tm bond frac.', 'BondFractions|Ca - Nb bond frac.', 'XRDPowderPattern|xrd_82', 'BondFractions|Ce - Mg bond frac.', 'BondFractions|Ce - P bond frac.', 'BondFractions|Cl - Eu bond frac.', 'BondFractions|Be - H bond frac.', 'BondFractions|I - Se bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 166', 'BondFractions|Pb - Sb bond frac.', 'BondFractions|Cr - Sr bond frac.', 'BondFractions|Ba - Cd bond frac.', 'BondFractions|Ca - La bond frac.', 'BondFractions|Re - Tb bond frac.', 'BondFractions|In - U bond frac.', 'BondFractions|Al - Rh bond frac.', 'BondFractions|Ce - Tl bond frac.', 'BondFractions|Sr - Tm bond frac.', 'BondFractions|Fe - Nb bond frac.', 'BondFractions|La - O bond frac.', 'BondFractions|Si - Yb bond frac.', 'BondFractions|Pm - Pm bond frac.', 'BondFractions|Pb - Y bond frac.', 'BondFractions|Sb - Tc bond frac.', 'BondFractions|Cl - Xe bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 73', 'BondFractions|Pr - W bond frac.', 'BondFractions|Ga - Sc bond frac.', 'BondFractions|Br - V bond frac.', 'BondFractions|Al - Sm bond frac.', 'BondFractions|Mg - Pm bond frac.', 'CoulombMatrix|coulomb matrix eig 232', 'BondFractions|Mg - U bond frac.', 'BondFractions|Be - Mo bond frac.', 'BondFractions|Th - Th bond frac.', 'BondFractions|Ca - Sn bond frac.', 'BondFractions|Ag - Na bond frac.', 'BondFractions|Si - Tc bond frac.', 'XRDPowderPattern|xrd_56', 'BondFractions|Gd - Ni bond frac.', 'CoulombMatrix|coulomb matrix eig 99', 'BondFractions|Bi - U bond frac.', 'BondFractions|V - Yb bond frac.', 'BondFractions|Ca - Gd bond frac.', 'BondFractions|Ag - V bond frac.', 'CoulombMatrix|coulomb matrix eig 163', 'BondFractions|Fe - Ir bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 124', 'BondFractions|B - Rb bond frac.', 'BondFractions|Fe - Pu bond frac.', 'BondFractions|Nb - Nb bond frac.', 'BondFractions|Pu - W bond frac.', 'BondFractions|As - Np bond frac.', 'BondFractions|Cd - Ga bond frac.', 'BondFractions|H - I bond frac.', 'BondFractions|Mo - Pa bond frac.', 'BondFractions|Ac - Sr bond frac.', 'BondFractions|B - Xe bond frac.', 'BondFractions|Cu - Nb bond frac.', 'BondFractions|Tb - Tb bond frac.', 'BondFractions|Be - U bond frac.', 'BondFractions|Pt - Re bond frac.', 'BondFractions|Cr - Pd bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 232', 'BondFractions|Cd - Cu bond frac.', 'BondFractions|Tc - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 58', 'BondFractions|Cl - Ge bond frac.', 'BondFractions|Ge - Rb bond frac.', 'BondFractions|Ir - U bond frac.', 'BondFractions|Ca - U bond frac.', 'BondFractions|F - Re bond frac.', 'BondFractions|Ho - Si bond frac.', 'BondFractions|Sr - Ti bond frac.', 'BondFractions|Mo - P bond frac.', 'BondFractions|Ac - Ca bond frac.', 'BondFractions|Al - Ta bond frac.', 'BondFractions|Dy - Lu bond frac.', 'BondFractions|Na - Rb bond frac.', 'BondFractions|Cu - Pr bond frac.', 'BondFractions|La - Nd bond frac.', 'BondFractions|Mg - Sb bond frac.', 'BondFractions|N - Yb bond frac.', 'BondFractions|Bi - In bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 40', 'BondFractions|Ga - Mg bond frac.', 'BondFractions|Er - Pa bond frac.', 'BondFractions|Hg - Pa bond frac.', 'BondFractions|Ba - Ca bond frac.', 'BondFractions|Al - Tm bond frac.', 'BondFractions|Ge - Pb bond frac.', 'BondFractions|Hg - Tm bond frac.', 'BondFractions|I - La bond frac.', 'BondFractions|Ir - O bond frac.', 'BondFractions|Ti - Tl bond frac.', 'BondFractions|Nb - Nd bond frac.', 'BondFractions|Ge - I bond frac.', 'XRDPowderPattern|xrd_68', 'BondFractions|Cd - Ti bond frac.', 'CoulombMatrix|coulomb matrix eig 241', 'BondFractions|Np - O bond frac.', 'BondFractions|Ta - V bond frac.', 'BondFractions|Re - Si bond frac.', 'BondFractions|B - U bond frac.', 'BondFractions|Br - Nd bond frac.', 'BondFractions|La - Sm bond frac.', 'BondFractions|Cl - Tm bond frac.', 'BondFractions|Ge - Se bond frac.', 'BondFractions|Al - Yb bond frac.', 'BondFractions|La - Ti bond frac.', 'BondFractions|In - Ti bond frac.', 'BondFractions|Ac - Ir bond frac.', 'BondFractions|Ge - Zr bond frac.', 'BondFractions|Mg - Re bond frac.', 'BondFractions|Fe - Pr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 150', 'BondFractions|Pb - Th bond frac.', 'BondFractions|Rb - Tb bond frac.', 'BondFractions|Gd - Tl bond frac.', 'BondFractions|Gd - Os bond frac.', 'BondFractions|F - Rb bond frac.', 'BondFractions|Cd - Te bond frac.', 'BondFractions|Cs - Te bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 161', 'BondFractions|Pr - Tm bond frac.', 'BondFractions|Li - Pa bond frac.', 'BondFractions|Dy - Pa bond frac.', 'BondFractions|Pm - Tl bond frac.', 'BondFractions|Fe - Tb bond frac.', 'BondFractions|K - Nb bond frac.', 'BondFractions|Ce - Pr bond frac.', 'BondFractions|F - Ga bond frac.', 'BondFractions|Ge - Hf bond frac.', 'BondFractions|Mo - Sc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 97', 'BondFractions|Ga - La bond frac.', 'BondFractions|Pa - Re bond frac.', 'BondFractions|Cs - Tb bond frac.', 'BondFractions|O - Yb bond frac.', 'BondFractions|Lu - Pd bond frac.', 'BondFractions|Na - Pd bond frac.', 'BondFractions|Re - Zr bond frac.', 'BondFractions|Au - Y bond frac.', 'BondFractions|Ta - U bond frac.', 'BondFractions|Lu - Ti bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 231', 'BondFractions|Cd - Tm bond frac.', 'BondFractions|Fe - Pb bond frac.', 'BondFractions|Ca - Pt bond frac.', 'BondFractions|Ce - K bond frac.', 'BondFractions|Bi - Nd bond frac.', 'BondFractions|Cs - Tm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 198', 'BondFractions|Au - Sn bond frac.', 'BondFractions|Mn - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 67', 'BondFractions|Pt - Zr bond frac.', 'BondFractions|Mn - Np bond frac.', 'BondFractions|Au - Yb bond frac.', 'BondFractions|Ca - Sm bond frac.', 'BondFractions|Ta - Yb bond frac.', 'BondFractions|Cd - Sc bond frac.', 'BondFractions|Co - Pu bond frac.', 'BondFractions|Ge - Sn bond frac.', 'BondFractions|Se - Te bond frac.', 'XRDPowderPattern|xrd_94', 'BondFractions|Rh - Sn bond frac.', 'BondFractions|Na - Re bond frac.', 'BondFractions|Cl - Ho bond frac.', 'CoulombMatrix|coulomb matrix eig 124', 'SineCoulombMatrix|sine coulomb matrix eig 243', 'BondFractions|K - U bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 102', 'BondFractions|Pb - Tc bond frac.', 'BondFractions|As - I bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 138', 'BondFractions|Nd - Zn bond frac.', 'BondFractions|Cd - O bond frac.', 'BondFractions|Cr - Gd bond frac.', 'BondFractions|Bi - Cs bond frac.', 'BondFractions|Pu - Rh bond frac.', 'BondFractions|Mn - Th bond frac.', 'BondFractions|Cs - Os bond frac.', 'BondFractions|Dy - Pr bond frac.', 'CoulombMatrix|coulomb matrix eig 136', 'BondFractions|Ga - In bond frac.', 'BondFractions|Fe - Ge bond frac.', 'BondFractions|Co - Nd bond frac.', 'BondFractions|I - Pt bond frac.', 'BondFractions|K - Os bond frac.', 'BondFractions|Pa - Ru bond frac.', 'BondFractions|U - U bond frac.', 'BondFractions|N - Zr bond frac.', 'BondFractions|Al - Ce bond frac.', 'CoulombMatrix|coulomb matrix eig 127', 'BondFractions|Ca - W bond frac.', 'BondFractions|Np - Np bond frac.', 'BondFractions|Si - Te bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 71', 'BondFractions|Cu - Pa bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 193', 'BondFractions|Ho - La bond frac.', 'BondFractions|As - Ti bond frac.', 'BondFractions|Dy - Nb bond frac.', 'BondFractions|Au - Zn bond frac.', 'BondFractions|La - Zn bond frac.', 'BondFractions|Eu - W bond frac.', 'BondFractions|Ag - Tl bond frac.', 'BondFractions|Pm - Sn bond frac.', 'CoulombMatrix|coulomb matrix eig 87', 'BondFractions|Ce - Hg bond frac.', 'BondFractions|Co - Nb bond frac.', 'BondFractions|Ca - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 147', 'BondFractions|Th - Y bond frac.', 'BondFractions|Ac - Zn bond frac.', 'BondFractions|Nb - Rb bond frac.', 'BondFractions|As - Zr bond frac.', 'BondFractions|Ir - Ni bond frac.', 'CoulombMatrix|coulomb matrix eig 44', 'BondFractions|Th - Tl bond frac.', 'BondFractions|O - Sm bond frac.', 'BondFractions|Co - Er bond frac.', 'BondFractions|Sb - Si bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 183', 'BondFractions|Re - V bond frac.', 'CoulombMatrix|coulomb matrix eig 182', 'BondFractions|Te - Y bond frac.', 'BondFractions|Ag - Pt bond frac.', 'BondFractions|Ce - Cu bond frac.', 'BondFractions|In - Sb bond frac.', 'BondFractions|As - Cr bond frac.', 'BondFractions|Sn - Sr bond frac.', 'BondFractions|H - P bond frac.', 'BondFractions|H - Pb bond frac.', 'BondFractions|F - Xe bond frac.', 'BondFractions|Li - Tl bond frac.', 'BondFractions|B - Ir bond frac.', 'BondFractions|Eu - Nd bond frac.', 'BondFractions|Y - Zn bond frac.', 'BondFractions|B - Ta bond frac.', 'BondFractions|Tb - V bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 227', 'BondFractions|Bi - Co bond frac.', 'BondFractions|Br - K bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 163', 'BondFractions|Nb - Sn bond frac.', 'BondFractions|Ge - Mn bond frac.', 'BondFractions|Co - H bond frac.', 'BondFractions|Ta - Tl bond frac.', 'BondFractions|Sn - Y bond frac.', 'BondFractions|Te - Tm bond frac.', 'BondFractions|F - Ta bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 135', 'SineCoulombMatrix|sine coulomb matrix eig 29', 'BondFractions|Ga - Si bond frac.', 'BondFractions|Hf - Rb bond frac.', 'XRDPowderPattern|xrd_54', 'BondFractions|Ru - Sc bond frac.', 'BondFractions|Ir - Sr bond frac.', 'BondFractions|Cd - Na bond frac.', 'BondFractions|O - Se bond frac.', 'BondFractions|Eu - Rh bond frac.', 'BondFractions|B - Y bond frac.', 'BondFractions|Pm - Zr bond frac.', 'BondFractions|Cd - Hg bond frac.', 'BondFractions|Hf - Pt bond frac.', 'BondFractions|Sr - Y bond frac.', 'BondFractions|Re - Sn bond frac.', 'BondFractions|Ga - Zr bond frac.', 'BondFractions|Ag - I bond frac.', 'BondFractions|B - Tc bond frac.', 'BondFractions|Be - Rb bond frac.', 'BondFractions|Mg - Pb bond frac.', 'CoulombMatrix|coulomb matrix eig 252', 'BondFractions|Nb - Yb bond frac.', 'BondFractions|Al - Tb bond frac.', 'BondFractions|Er - F bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 145', 'BondFractions|Pr - Sr bond frac.', 'BondFractions|I - Mo bond frac.', 'BondFractions|Ac - Ti bond frac.', 'BondFractions|P - Pr bond frac.', 'BondFractions|Y - Yb bond frac.', 'BondFractions|Cs - Sc bond frac.', 'BondFractions|La - S bond frac.', 'BondFractions|Mg - Tl bond frac.', 'BondFractions|Al - In bond frac.', 'BondFractions|Sm - Zn bond frac.', 'BondFractions|Mg - Sr bond frac.', 'XRDPowderPattern|xrd_8', 'BondFractions|Gd - Pb bond frac.', 'BondFractions|Ir - Ta bond frac.', 'BondFractions|Fe - U bond frac.', 'BondFractions|Pu - Sm bond frac.', 'BondFractions|Ag - W bond frac.', 'BondFractions|Li - Np bond frac.', 'BondFractions|Rh - Th bond frac.', 'BondFractions|La - Sb bond frac.', 'BondFractions|Ca - Sr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 182', 'BondFractions|U - Yb bond frac.', 'BondFractions|Ta - W bond frac.', 'BondFractions|Re - Tl bond frac.', 'BondFractions|Na - Sb bond frac.', 'BondFractions|Na - Yb bond frac.', 'BondFractions|Fe - Zr bond frac.', 'BondFractions|Au - In bond frac.', 'CoulombMatrix|coulomb matrix eig 156', 'BondFractions|Al - Ba bond frac.', 'BondFractions|F - Tb bond frac.', 'BondFractions|Rb - Si bond frac.', 'BondFractions|Hf - Rh bond frac.', 'CoulombMatrix|coulomb matrix eig 225', 'BondFractions|Se - Zn bond frac.', 'BondFractions|Sr - Tc bond frac.', 'BondFractions|Al - Dy bond frac.', 'BondFractions|Al - Np bond frac.', 'BondFractions|Hg - Pr bond frac.', 'BondFractions|Co - Dy bond frac.', 'BondFractions|Ru - W bond frac.', 'BondFractions|C - Mo bond frac.', 'BondFractions|C - Pa bond frac.', 'BondFractions|Ag - Ir bond frac.', 'CoulombMatrix|coulomb matrix eig 185', 'BondFractions|As - Ta bond frac.', 'BondFractions|Fe - Mo bond frac.', 'BondFractions|Bi - I bond frac.', 'CoulombMatrix|coulomb matrix eig 281', 'BondFractions|Au - Pt bond frac.', 'CoulombMatrix|coulomb matrix eig 249', 'BondFractions|Co - Pb bond frac.', 'BondFractions|F - Ru bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 291', 'XRDPowderPattern|xrd_114', 'CoulombMatrix|coulomb matrix eig 71', 'BondFractions|Rb - Sr bond frac.', 'BondFractions|Rb - S bond frac.', 'CoulombMatrix|coulomb matrix eig 189', 'BondFractions|Gd - Nb bond frac.', 'BondFractions|C - Tc bond frac.', 'BondFractions|In - Pd bond frac.', 'BondFractions|Cd - Ru bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 282', 'BondFractions|Os - Sr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 133', 'BondFractions|Pt - Sn bond frac.', 'BondFractions|Tc - Y bond frac.', 'CoulombMatrix|coulomb matrix eig 86', 'BondFractions|Au - Li bond frac.', 'BondFractions|Os - Ru bond frac.', 'BondFractions|Fe - Rb bond frac.', 'BondFractions|Br - Ru bond frac.', 'BondFractions|P - Pd bond frac.', 'BondFractions|Nd - Sm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 172', 'BondFractions|La - Li bond frac.', 'BondFractions|Au - Ce bond frac.', 'BondFractions|Bi - P bond frac.', 'BondFractions|Hf - N bond frac.', 'BondFractions|Mn - Ta bond frac.', 'BondFractions|Pt - Tl bond frac.', 'BondFractions|Tc - Yb bond frac.', 'BondFractions|C - Nd bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 197', 'BondFractions|Mg - Se bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 32', 'BondFractions|Cd - Hf bond frac.', 'BondFractions|Be - Ir bond frac.', 'BondFractions|Ho - Ir bond frac.', 'BondFractions|Re - Yb bond frac.', 'BondFractions|Ge - Ta bond frac.', 'BondFractions|Br - Np bond frac.', 'BondFractions|Cr - Tb bond frac.', 'BondFractions|Be - Cd bond frac.', 'BondFractions|C - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 32', 'BondFractions|H - Ho bond frac.', 'CoulombMatrix|coulomb matrix eig 100', 'BondFractions|Br - N bond frac.', 'BondFractions|Cr - Np bond frac.', 'BondFractions|Cd - Nd bond frac.', 'BondFractions|B - Pa bond frac.', 'BondFractions|Ca - In bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 240', 'BondFractions|Ge - Mo bond frac.', 'CoulombMatrix|coulomb matrix eig 187', 'BondFractions|Eu - Sb bond frac.', 'BondFractions|Mn - Pb bond frac.', 'CoulombMatrix|coulomb matrix eig 65', 'BondFractions|Br - Yb bond frac.', 'BondFractions|Th - Ti bond frac.', 'BondFractions|Mo - Tc bond frac.', 'CoulombMatrix|coulomb matrix eig 219', 'BondFractions|Tm - Yb bond frac.', 'BondFractions|Tb - Ti bond frac.', 'BondFractions|Mo - Nb bond frac.', 'BondFractions|Be - Sr bond frac.', 'BondFractions|Gd - Hg bond frac.', 'BondFractions|Ru - Xe bond frac.', 'BondFractions|Mo - Ni bond frac.', 'BondFractions|Ca - Os bond frac.', 'BondFractions|V - Y bond frac.', 'BondFractions|Br - Cl bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 278', 'BondFractions|Gd - Yb bond frac.', 'BondFractions|Sr - V bond frac.', 'BondFractions|Cr - Cs bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 262', 'XRDPowderPattern|xrd_70', 'CoulombMatrix|coulomb matrix eig 275', 'BondFractions|Au - Ga bond frac.', 'BondFractions|H - Ti bond frac.', 'BondFractions|Au - N bond frac.', 'BondFractions|Ba - N bond frac.', 'BondFractions|I - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 99', 'BondFractions|La - Tc bond frac.', 'BondFractions|Gd - Rh bond frac.', 'BondFractions|Gd - P bond frac.', 'BondFractions|P - W bond frac.', 'BondFractions|Pu - Sr bond frac.', 'BondFractions|Te - Yb bond frac.', 'BondFractions|H - Tc bond frac.', 'BondFractions|Ba - Tm bond frac.', 'BondFractions|La - Tl bond frac.', 'BondFractions|O - Tc bond frac.', 'BondFractions|Ag - O bond frac.', 'BondFractions|Rh - Sr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 233', 'BondFractions|Pt - Tb bond frac.', 'BondFractions|Ho - Tb bond frac.', 'BondFractions|Bi - Si bond frac.', 'BondFractions|Cd - Sr bond frac.', 'BondFractions|Au - K bond frac.', 'BondFractions|Zr - Zr bond frac.', 'BondFractions|Dy - W bond frac.', 'BondFractions|Rb - Tm bond frac.', 'BondFractions|Cd - Sn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 106', 'BondFractions|In - Re bond frac.', 'BondFractions|O - W bond frac.', 'BondFractions|Hg - Pm bond frac.', 'BondFractions|Au - Ir bond frac.', 'CoulombMatrix|coulomb matrix eig 291', 'BondFractions|Ce - La bond frac.', 'BondFractions|Sc - Tb bond frac.', 'CoulombMatrix|coulomb matrix eig 102', 'BondFractions|Mg - Tm bond frac.', 'BondFractions|Sm - Y bond frac.', 'BondFractions|F - W bond frac.', 'BondFractions|Ga - Pt bond frac.', 'BondFractions|Li - Mo bond frac.', 'BondFractions|Cs - Xe bond frac.', 'BondFractions|P - Tl bond frac.', 'BondFractions|Pd - Th bond frac.', 'CoulombMatrix|coulomb matrix eig 33', 'CoulombMatrix|coulomb matrix eig 211', 'BondFractions|Ga - S bond frac.', 'BondFractions|Sr - Sr bond frac.', 'XRDPowderPattern|xrd_42', 'BondFractions|W - Y bond frac.', 'BondFractions|Rb - Zr bond frac.', 'BondFractions|I - Nb bond frac.', 'BondFractions|Ga - Pu bond frac.', 'CoulombMatrix|coulomb matrix eig 119', 'BondFractions|Au - Ti bond frac.', 'BondFractions|Sn - W bond frac.', 'XRDPowderPattern|xrd_120', 'SineCoulombMatrix|sine coulomb matrix eig 157', 'BondFractions|Fe - Lu bond frac.', 'BondFractions|Ru - V bond frac.', 'BondFractions|Li - Lu bond frac.', 'BondFractions|As - Ba bond frac.', 'BondFractions|O - Pb bond frac.', 'BondFractions|I - Si bond frac.', 'BondFractions|Re - S bond frac.', 'BondFractions|Be - Pb bond frac.', 'BondFractions|Np - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 201', 'BondFractions|Dy - U bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 90', 'BondFractions|Nd - Sb bond frac.', 'BondFractions|Sr - Ta bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 86', 'BondFractions|Ca - Ce bond frac.', 'BondFractions|Cl - Tb bond frac.', 'BondFractions|H - Pr bond frac.', 'BondFractions|Cs - Sn bond frac.', 'BondFractions|Ho - Mg bond frac.', 'BondFractions|Co - Sn bond frac.', 'BondFractions|Sr - Th bond frac.', 'BondFractions|Er - Tl bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 261', 'BondFractions|Dy - Ti bond frac.', 'BondFractions|Ru - Ru bond frac.', 'BondFractions|Cl - H bond frac.', 'BondFractions|Ce - Ni bond frac.', 'CoulombMatrix|coulomb matrix eig 253', 'BondFractions|As - U bond frac.', 'CoulombMatrix|coulomb matrix eig 129', 'BondFractions|K - Th bond frac.', 'BondFractions|Er - Ho bond frac.', 'BondFractions|Mg - Np bond frac.', 'XRDPowderPattern|xrd_46', 'BondFractions|Bi - Pt bond frac.', 'BondFractions|Cd - W bond frac.', 'BondFractions|As - Os bond frac.', 'BondFractions|As - Eu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 93', 'BondFractions|Ba - O bond frac.', 'BondFractions|Ac - Pb bond frac.', 'BondFractions|C - Y bond frac.', 'BondFractions|Dy - Dy bond frac.', 'BondFractions|Re - Rh bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 250', 'BondFractions|Re - Sr bond frac.', 'BondFractions|Ga - Ir bond frac.', 'BondFractions|Fe - Sr bond frac.', 'BondFractions|Ac - Ag bond frac.', 'BondFractions|Lu - Pa bond frac.', 'BondFractions|Ho - Ho bond frac.', 'BondFractions|Se - Tb bond frac.', 'BondFractions|Pd - Si bond frac.', 'BondFractions|Cr - Xe bond frac.', 'BondFractions|F - Te bond frac.', 'BondFractions|Sb - Sr bond frac.', 'CoulombMatrix|coulomb matrix eig 95', 'BondFractions|Tc - W bond frac.', 'BondFractions|Bi - Xe bond frac.', 'BondFractions|Cl - Pu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 242', 'BondFractions|Ce - Pd bond frac.', 'BondFractions|Eu - Lu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 229', 'BondFractions|F - Ho bond frac.', 'BondFractions|Pb - Sn bond frac.', 'BondFractions|Ce - Mo bond frac.', 'CoulombMatrix|coulomb matrix eig 25', 'BondFractions|S - Sn bond frac.', 'BondFractions|Ac - Ga bond frac.', 'BondFractions|Ba - Ni bond frac.', 'BondFractions|I - Li bond frac.', 'BondFractions|Nb - Sc bond frac.', 'CoulombMatrix|coulomb matrix eig 194', 'BondFractions|Pa - S bond frac.', 'BondFractions|Ba - Ce bond frac.', 'BondFractions|N - Ta bond frac.', 'BondFractions|Ac - Cl bond frac.', 'BondFractions|Ga - O bond frac.', 'BondFractions|Lu - Tc bond frac.', 'BondFractions|F - Kr bond frac.', 'BondFractions|Pa - Tm bond frac.', 'BondFractions|Cr - Th bond frac.', 'BondFractions|Cu - Re bond frac.', 'CoulombMatrix|coulomb matrix eig 103', 'BondFractions|Pm - Y bond frac.', 'BondFractions|Cs - Sr bond frac.', 'BondFractions|Ac - Rh bond frac.', 'BondFractions|Se - Sm bond frac.', 'BondFractions|Na - Pr bond frac.', 'BondFractions|Pt - Tm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 100', 'BondFractions|Ga - Tm bond frac.', 'BondFractions|Rh - Ti bond frac.', 'BondFractions|Cs - Pt bond frac.', 'BondFractions|F - Sr bond frac.', 'BondFractions|I - Tb bond frac.', 'CoulombMatrix|coulomb matrix eig 53', 'BondFractions|Al - Gd bond frac.', 'BondFractions|Mn - Tm bond frac.', 'BondFractions|Ba - P bond frac.', 'CoulombMatrix|coulomb matrix eig 79', 'XRDPowderPattern|xrd_14', 'BondFractions|Br - P bond frac.', 'BondFractions|Be - Sm bond frac.', 'BondFractions|B - Ge bond frac.', 'BondFractions|Au - C bond frac.', 'BondFractions|Pm - Ru bond frac.', 'BondFractions|Pb - Zn bond frac.', 'BondFractions|Pt - Sb bond frac.', 'BondFractions|Cs - In bond frac.', 'BondFractions|In - K bond frac.', 'BondFractions|Ag - Pd bond frac.', 'BondFractions|La - Te bond frac.', 'BondFractions|Ni - Pu bond frac.', 'BondFractions|Na - Os bond frac.', 'BondFractions|Au - Te bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 169', 'BondFractions|Pu - Se bond frac.', 'BondFractions|Ag - As bond frac.', 'BondFractions|As - Ir bond frac.', 'BondFractions|Re - Sb bond frac.', 'BondFractions|Pr - Sc bond frac.', 'BondFractions|Al - Sb bond frac.', 'BondFractions|F - Pb bond frac.', 'BondFractions|Pb - S bond frac.', 'BondFractions|Ca - Nd bond frac.', 'BondFractions|I - Nd bond frac.', 'BondFractions|H - In bond frac.', 'BondFractions|Ag - Rb bond frac.', 'BondFractions|O - Pt bond frac.', 'BondFractions|Ga - Os bond frac.', 'BondFractions|Ho - Te bond frac.', 'BondFractions|Cr - Ge bond frac.', 'BondFractions|B - Te bond frac.', 'BondFractions|Cs - Ir bond frac.', 'BondFractions|Lu - S bond frac.', 'BondFractions|Gd - Sc bond frac.', 'BondFractions|Er - Pd bond frac.', 'BondFractions|Os - U bond frac.', 'XRDPowderPattern|xrd_98', 'BondFractions|Au - S bond frac.', 'CoulombMatrix|coulomb matrix eig 289', 'BondFractions|B - Tl bond frac.', 'BondFractions|Cl - U bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 221', 'BondFractions|Nb - Th bond frac.', 'BondFractions|Dy - Te bond frac.', 'BondFractions|Ho - Sb bond frac.', 'BondFractions|Er - Lu bond frac.', 'BondFractions|Pd - Pm bond frac.', 'BondFractions|In - Mn bond frac.', 'BondFractions|Ce - Rb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 11', 'BondFractions|Bi - Zn bond frac.', 'BondFractions|Be - Gd bond frac.', 'CoulombMatrix|coulomb matrix eig 205', 'SineCoulombMatrix|sine coulomb matrix eig 295', 'BondFractions|Lu - Ni bond frac.', 'BondFractions|Cr - Zr bond frac.', 'BondFractions|Pb - Pt bond frac.', 'BondFractions|Cs - Ga bond frac.', 'BondFractions|Co - Tb bond frac.', 'BondFractions|Co - Sr bond frac.', 'BondFractions|Cl - W bond frac.', 'BondFractions|As - Rb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 199', 'CoulombMatrix|coulomb matrix eig 17', 'BondFractions|N - Nd bond frac.', 'BondFractions|B - Se bond frac.', 'BondFractions|Hf - Zn bond frac.', 'BondFractions|Br - Sb bond frac.', 'BondFractions|Cu - Tm bond frac.', 'BondFractions|Lu - Re bond frac.', 'BondFractions|As - B bond frac.', 'BondFractions|Li - Pd bond frac.', 'BondFractions|Pt - Si bond frac.', 'BondFractions|Cs - Zn bond frac.', 'BondFractions|K - Yb bond frac.', 'BondFractions|Nb - Te bond frac.', 'BondFractions|Ge - Na bond frac.', 'BondFractions|Hf - Re bond frac.', 'BondFractions|Al - Pr bond frac.', 'BondFractions|B - Hf bond frac.', 'BondFractions|Br - Eu bond frac.', 'BondFractions|Mo - Rb bond frac.', 'BondFractions|In - Mg bond frac.', 'BondFractions|B - Zr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 42', 'BondFractions|In - O bond frac.', 'BondFractions|Dy - Si bond frac.', 'BondFractions|In - S bond frac.', 'BondFractions|O - Os bond frac.', 'BondFractions|Rh - Tc bond frac.', 'BondFractions|In - Te bond frac.', 'BondFractions|Nd - Ti bond frac.', 'BondFractions|I - Re bond frac.', 'BondFractions|Eu - Mo bond frac.', 'BondFractions|Pr - Ru bond frac.', 'CoulombMatrix|coulomb matrix eig 265', 'BondFractions|Os - Pb bond frac.', 'BondFractions|Cl - Nb bond frac.', 'BondFractions|Ag - Tb bond frac.', 'BondFractions|As - Re bond frac.', 'BondFractions|Cd - Rb bond frac.', 'CoulombMatrix|coulomb matrix eig 168', 'BondFractions|O - Rh bond frac.', 'BondFractions|Fe - Hf bond frac.', 'BondFractions|Er - Ti bond frac.', 'BondFractions|Si - Ta bond frac.', 'BondFractions|Ga - I bond frac.', 'BondFractions|Cu - Sm bond frac.', 'BondFractions|Hf - Os bond frac.', 'BondFractions|Mo - Re bond frac.', 'BondFractions|Cr - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 94', 'BondFractions|Au - Pu bond frac.', 'BondFractions|C - Eu bond frac.', 'BondFractions|O - Tm bond frac.', 'BondFractions|Hg - Mn bond frac.', 'XRDPowderPattern|xrd_40', 'BondFractions|B - Gd bond frac.', 'BondFractions|Ta - Zr bond frac.', 'BondFractions|Te - Th bond frac.', 'BondFractions|Ga - Pa bond frac.', 'BondFractions|F - Pd bond frac.', 'BondFractions|In - P bond frac.', 'BondFractions|Ce - Pb bond frac.', 'BondFractions|Ta - Tb bond frac.', 'BondFractions|Ru - Tm bond frac.', 'CoulombMatrix|coulomb matrix eig 155', 'BondFractions|Mn - Sn bond frac.', 'BondFractions|Er - Zn bond frac.', 'BondFractions|Eu - Zn bond frac.', 'CoulombMatrix|coulomb matrix eig 170', 'BondFractions|I - U bond frac.', 'BondFractions|Eu - Ni bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 111', 'BondFractions|Hf - Hf bond frac.', 'BondFractions|Ba - La bond frac.', 'BondFractions|Ga - Zn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 246', 'BondFractions|Cs - Cu bond frac.', 'BondFractions|Rh - Y bond frac.', 'BondFractions|Se - Ta bond frac.', 'BondFractions|Eu - Tb bond frac.', 'BondFractions|Te - Xe bond frac.', 'BondFractions|Ba - In bond frac.', 'BondFractions|Pd - Pd bond frac.', 'BondFractions|Er - Sn bond frac.', 'BondFractions|Ca - Lu bond frac.', 'BondFractions|Cs - Np bond frac.', 'BondFractions|Tl - Y bond frac.', 'BondFractions|H - Np bond frac.', 'BondFractions|Ag - S bond frac.', 'BondFractions|Rb - Rb bond frac.', 'BondFractions|Be - Se bond frac.', 'BondFractions|Ce - Ta bond frac.', 'BondFractions|Gd - Zn bond frac.', 'BondFractions|Ga - Tl bond frac.', 'BondFractions|Ni - Pd bond frac.', 'BondFractions|P - Yb bond frac.', 'BondFractions|Hf - I bond frac.', 'CoulombMatrix|coulomb matrix eig 257', 'BondFractions|Cl - Er bond frac.', 'BondFractions|Sb - Te bond frac.', 'BondFractions|Bi - Ge bond frac.', 'BondFractions|As - Tl bond frac.', 'BondFractions|Na - Ta bond frac.', 'BondFractions|Ho - W bond frac.', 'BondFractions|Pu - Tm bond frac.', 'BondFractions|Cd - Rh bond frac.', 'BondFractions|Gd - Rb bond frac.', 'BondFractions|Cs - Si bond frac.', 'BondFractions|Cu - W bond frac.', 'BondFractions|Ge - Y bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 26', 'BondFractions|Ho - Na bond frac.', 'BondFractions|P - Rb bond frac.', 'BondFractions|Bi - Sn bond frac.', 'BondFractions|Pd - Pu bond frac.', 'BondFractions|I - Yb bond frac.', 'BondFractions|Fe - Tl bond frac.', 'CoulombMatrix|coulomb matrix eig 259', 'BondFractions|La - Rh bond frac.', 'BondFractions|Mo - Tm bond frac.', 'CoulombMatrix|coulomb matrix eig 120', 'BondFractions|Be - In bond frac.', 'BondFractions|In - Nb bond frac.', 'BondFractions|Er - Li bond frac.', 'BondFractions|I - Mg bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 237', 'BondFractions|H - Zr bond frac.', 'BondFractions|Cs - Y bond frac.', 'BondFractions|Lu - Pb bond frac.', 'BondFractions|Al - Au bond frac.', 'CoulombMatrix|coulomb matrix eig 161', 'BondFractions|Ir - Re bond frac.', 'BondFractions|Lu - Ru bond frac.', 'CoulombMatrix|coulomb matrix eig 80', 'BondFractions|F - Y bond frac.', 'BondFractions|Er - Y bond frac.', 'BondFractions|B - Tm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 17', 'BondFractions|Mn - Te bond frac.', 'BondFractions|Ho - Zn bond frac.', 'BondFractions|Er - I bond frac.', 'BondFractions|In - Os bond frac.', 'BondFractions|Ga - Sm bond frac.', 'BondFractions|Sn - Tb bond frac.', 'BondFractions|H - Te bond frac.', 'BondFractions|Ge - Hg bond frac.', 'BondFractions|Te - Zn bond frac.', 'BondFractions|Te - Tl bond frac.', 'BondFractions|Mo - Sm bond frac.', 'BondFractions|Dy - Ru bond frac.', 'BondFractions|Nb - Pr bond frac.', 'BondFractions|Eu - Ga bond frac.', 'BondFractions|Er - Os bond frac.', 'BondFractions|Mn - Tl bond frac.', 'BondFractions|Ru - Th bond frac.', 'BondFractions|Ru - Ta bond frac.', 'BondFractions|Na - Zr bond frac.', 'BondFractions|As - Ga bond frac.', 'BondFractions|Lu - Sn bond frac.', 'BondFractions|Cr - Pb bond frac.', 'XRDPowderPattern|xrd_30', 'BondFractions|Br - W bond frac.', 'BondFractions|B - Tb bond frac.', 'BondFractions|Cr - Pu bond frac.', 'BondFractions|Mn - Pa bond frac.', 'BondFractions|Dy - Pb bond frac.', 'BondFractions|Nd - Pb bond frac.', 'BondFractions|Nb - Xe bond frac.', 'BondFractions|Dy - Fe bond frac.', 'BondFractions|As - Au bond frac.', 'BondFractions|F - Tl bond frac.', 'BondFractions|Ce - Dy bond frac.', 'BondFractions|Nd - Tm bond frac.', 'BondFractions|Ac - Cu bond frac.', 'BondFractions|Ho - Pd bond frac.', 'BondFractions|As - Dy bond frac.', 'BondFractions|B - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 230', 'BondFractions|Tc - V bond frac.', 'BondFractions|Bi - Cr bond frac.', 'BondFractions|Ag - In bond frac.', 'BondFractions|Cr - Er bond frac.', 'BondFractions|Cd - Cd bond frac.', 'BondFractions|I - Tl bond frac.', 'BondFractions|Er - Sm bond frac.', 'BondFractions|C - Ho bond frac.', 'BondFractions|Ir - Pd bond frac.', 'CoulombMatrix|coulomb matrix eig 221', 'BondFractions|Au - Xe bond frac.', 'BondFractions|Li - Ta bond frac.', 'BondFractions|In - Lu bond frac.', 'BondFractions|Ga - H bond frac.', 'BondFractions|Hg - Na bond frac.', 'BondFractions|Gd - S bond frac.', 'BondFractions|Ir - K bond frac.', 'BondFractions|Nb - Si bond frac.', 'BondFractions|Hg - Zr bond frac.', 'BondFractions|Os - Pr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 268', 'CoulombMatrix|coulomb matrix eig 214', 'XRDPowderPattern|xrd_60', 'BondFractions|Hf - Mn bond frac.', 'BondFractions|Eu - Yb bond frac.', 'BondFractions|Mo - Mo bond frac.', 'BondFractions|Re - Ru bond frac.', 'BondFractions|Be - Lu bond frac.', 'BondFractions|Ir - Nd bond frac.', 'BondFractions|Ba - Be bond frac.', 'BondFractions|Co - Ho bond frac.', 'BondFractions|Co - Mo bond frac.', 'BondFractions|B - H bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 61', 'BondFractions|Au - Pd bond frac.', 'BondFractions|Ag - Hg bond frac.', 'BondFractions|F - Zr bond frac.', 'BondFractions|Cs - Nb bond frac.', 'BondFractions|Nd - Pd bond frac.', 'CoulombMatrix|coulomb matrix eig 262', 'BondFractions|Ag - Th bond frac.', 'BondFractions|I - In bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 238', 'BondFractions|Ac - I bond frac.', 'BondFractions|Hf - Sm bond frac.', 'BondFractions|Mn - Zr bond frac.', 'BondFractions|B - La bond frac.', 'BondFractions|Hg - Sm bond frac.', 'BondFractions|Se - U bond frac.', 'BondFractions|Ru - U bond frac.', 'BondFractions|Ag - Ba bond frac.', 'BondFractions|Ir - W bond frac.', 'BondFractions|Os - Rh bond frac.', 'BondFractions|Pd - Y bond frac.', 'BondFractions|N - Pu bond frac.', 'BondFractions|Bi - Tm bond frac.', 'BondFractions|Mo - Pb bond frac.', 'BondFractions|Mg - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 89', 'BondFractions|In - Sm bond frac.', 'BondFractions|Ir - Sc bond frac.', 'BondFractions|Dy - Zr bond frac.', 'BondFractions|Bi - Pd bond frac.', 'BondFractions|Os - Sm bond frac.', 'BondFractions|Cs - Li bond frac.', 'BondFractions|C - Ta bond frac.', 'BondFractions|Co - Ru bond frac.', 'BondFractions|H - Lu bond frac.', 'BondFractions|Ho - Os bond frac.', 'BondFractions|Al - Sn bond frac.', 'BondFractions|Ni - Th bond frac.', 'BondFractions|Ni - Pt bond frac.', 'BondFractions|Li - Nd bond frac.', 'BondFractions|Ag - Cl bond frac.', 'BondFractions|Er - La bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 69', 'BondFractions|Co - Gd bond frac.', 'BondFractions|As - Pt bond frac.', 'CoulombMatrix|coulomb matrix eig 149', 'CoulombMatrix|coulomb matrix eig 145', 'BondFractions|Cd - Lu bond frac.', 'CoulombMatrix|coulomb matrix eig 63', 'BondFractions|Fe - La bond frac.', 'BondFractions|In - In bond frac.', 'BondFractions|Hf - Pa bond frac.', 'BondFractions|Ga - K bond frac.', 'BondFractions|Cu - Ir bond frac.', 'BondFractions|S - Te bond frac.', 'CoulombMatrix|coulomb matrix eig 139', 'BondFractions|B - I bond frac.', 'BondFractions|Eu - Ge bond frac.', 'BondFractions|Pa - Tl bond frac.', 'BondFractions|F - I bond frac.', 'BondFractions|Co - Ir bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 263', 'BondFractions|Fe - Se bond frac.', 'BondFractions|K - La bond frac.', 'BondFractions|F - Np bond frac.', 'BondFractions|Au - Ba bond frac.', 'BondFractions|Cd - Fe bond frac.', 'BondFractions|Ce - Tb bond frac.', 'BondFractions|Eu - Na bond frac.', 'BondFractions|Li - Pt bond frac.', 'BondFractions|U - Zn bond frac.', 'CoulombMatrix|coulomb matrix eig 26', 'BondFractions|Au - Be bond frac.', 'BondFractions|Pd - Re bond frac.', 'BondFractions|U - V bond frac.', 'BondFractions|Hg - Pb bond frac.', 'BondFractions|Ga - Ta bond frac.', 'BondFractions|Ge - Re bond frac.', 'BondFractions|Mg - Te bond frac.', 'BondFractions|Re - U bond frac.', 'BondFractions|Os - Ta bond frac.', 'BondFractions|Na - Tm bond frac.', 'BondFractions|Sm - Sm bond frac.', 'BondFractions|Bi - Ce bond frac.', 'BondFractions|Er - Ni bond frac.', 'BondFractions|In - Si bond frac.', 'BondFractions|Bi - Ca bond frac.', 'BondFractions|Au - Fe bond frac.', 'BondFractions|Nb - Tb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 22', 'BondFractions|Se - V bond frac.', 'BondFractions|Os - P bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 219', 'BondFractions|Cl - Lu bond frac.', 'BondFractions|As - As bond frac.', 'BondFractions|Li - Th bond frac.', 'BondFractions|Br - O bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 16', 'BondFractions|Nb - Tl bond frac.', 'BondFractions|In - Tc bond frac.', 'BondFractions|Na - W bond frac.', 'BondFractions|Mg - Pr bond frac.', 'BondFractions|Cu - I bond frac.', 'BondFractions|Mg - Rh bond frac.', 'BondFractions|Ac - Sn bond frac.', 'BondFractions|Ac - O bond frac.', 'BondFractions|Gd - Sb bond frac.', 'BondFractions|Ba - S bond frac.', 'BondFractions|Li - Pm bond frac.', 'BondFractions|Eu - F bond frac.', 'BondFractions|Bi - Sb bond frac.', 'BondFractions|Er - Nb bond frac.', 'BondFractions|Ce - Pu bond frac.', 'BondFractions|I - Sn bond frac.', 'BondFractions|B - Er bond frac.', 'BondFractions|In - Ir bond frac.', 'BondFractions|Er - Gd bond frac.', 'BondFractions|Kr - Kr bond frac.', 'BondFractions|In - Mo bond frac.', 'BondFractions|Br - Mg bond frac.', 'BondFractions|Dy - Os bond frac.', 'BondFractions|K - Pu bond frac.', 'BondFractions|I - Pb bond frac.', 'BondFractions|Ag - Er bond frac.', 'BondFractions|Cu - Tb bond frac.', 'BondFractions|Cs - Rb bond frac.', 'BondFractions|Lu - W bond frac.', 'BondFractions|Co - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 170', 'BondFractions|Lu - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 260', 'BondFractions|Pr - Tl bond frac.', 'BondFractions|Na - Sr bond frac.', 'BondFractions|Hg - La bond frac.', 'CoulombMatrix|coulomb matrix eig 171', 'BondFractions|Nb - S bond frac.', 'BondFractions|P - Tc bond frac.', 'BondFractions|Ir - S bond frac.', 'BondFractions|Nb - Pt bond frac.', 'BondFractions|Ge - U bond frac.', 'XRDPowderPattern|xrd_72', 'BondFractions|Hg - Te bond frac.', 'BondFractions|Sr - Tl bond frac.', 'BondFractions|Al - Nb bond frac.', 'CoulombMatrix|coulomb matrix eig 38', 'CoulombMatrix|coulomb matrix eig 24', 'BondFractions|Cd - Tb bond frac.', 'BondFractions|B - Rh bond frac.', 'BondFractions|Cl - Sn bond frac.', 'BondFractions|S - Y bond frac.', 'BondFractions|Hf - Ho bond frac.', 'CoulombMatrix|coulomb matrix eig 132', 'BondFractions|Ba - Zr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 47', 'BondFractions|Ta - Ta bond frac.', 'BondFractions|C - Ga bond frac.', 'BondFractions|Cd - Sm bond frac.', 'BondFractions|W - Zr bond frac.', 'BondFractions|Ca - Cs bond frac.', 'BondFractions|H - Th bond frac.', 'BondFractions|Ho - Tc bond frac.', 'CoulombMatrix|coulomb matrix eig 258', 'BondFractions|Cu - Xe bond frac.', 'BondFractions|Er - Fe bond frac.', 'BondFractions|In - Th bond frac.', 'BondFractions|Cu - Pt bond frac.', 'BondFractions|Li - Os bond frac.', 'BondFractions|Ni - Re bond frac.', 'BondFractions|N - W bond frac.', 'BondFractions|Ac - S bond frac.', 'BondFractions|Ce - U bond frac.', 'BondFractions|Ir - Np bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 113', 'BondFractions|Mo - Se bond frac.', 'BondFractions|La - Tm bond frac.', 'BondFractions|Ac - Cr bond frac.', 'BondFractions|Fe - Th bond frac.', 'XRDPowderPattern|xrd_36', 'BondFractions|Al - Nd bond frac.', 'BondFractions|F - Rh bond frac.', 'BondFractions|Na - Pm bond frac.', 'BondFractions|Ir - Pt bond frac.', 'BondFractions|Ca - Ir bond frac.', 'BondFractions|Ce - Tm bond frac.', 'BondFractions|Ag - Br bond frac.', 'CoulombMatrix|coulomb matrix eig 176', 'BondFractions|Pt - Ru bond frac.', 'BondFractions|Al - Pm bond frac.', 'BondFractions|F - Tc bond frac.', 'BondFractions|Rh - Tm bond frac.', 'BondFractions|H - U bond frac.', 'CoulombMatrix|coulomb matrix eig 295', 'BondFractions|Eu - O bond frac.', 'BondFractions|Ag - Y bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 281', 'BondFractions|C - Hg bond frac.', 'CoulombMatrix|coulomb matrix eig 288', 'BondFractions|Na - Nb bond frac.', 'BondFractions|Ce - Ge bond frac.', 'CoulombMatrix|coulomb matrix eig 206', 'BondFractions|H - Tl bond frac.', 'BondFractions|I - Ir bond frac.', 'BondFractions|K - Sr bond frac.', 'BondFractions|Hf - Sr bond frac.', 'BondFractions|Cr - Sm bond frac.', 'BondFractions|B - Sm bond frac.', 'BondFractions|Ir - Si bond frac.', 'BondFractions|Ho - Sm bond frac.', 'BondFractions|Hg - Hg bond frac.', 'XRDPowderPattern|xrd_86', 'BondFractions|Rb - V bond frac.', 'CoulombMatrix|coulomb matrix eig 23', 'BondFractions|La - Ru bond frac.', 'CoulombMatrix|coulomb matrix eig 184', 'CoulombMatrix|coulomb matrix eig 228', 'BondFractions|Al - Er bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 210', 'BondFractions|I - Pa bond frac.', 'XRDPowderPattern|xrd_76', 'BondFractions|C - Cs bond frac.', 'BondFractions|Bi - S bond frac.', 'BondFractions|Pb - Yb bond frac.', 'BondFractions|Dy - Eu bond frac.', 'BondFractions|Cd - Pd bond frac.', 'BondFractions|Bi - Dy bond frac.', 'BondFractions|Ag - F bond frac.', 'BondFractions|Al - Eu bond frac.', 'BondFractions|Rb - Sm bond frac.', 'BondFractions|P - Y bond frac.', 'BondFractions|Eu - Os bond frac.', 'BondFractions|Re - Se bond frac.', 'BondFractions|Ga - Ge bond frac.', 'BondFractions|Gd - Sn bond frac.', 'BondFractions|Cl - Ru bond frac.', 'BondFractions|Os - Ti bond frac.', 'BondFractions|Ge - Sr bond frac.', 'BondFractions|Ge - P bond frac.', 'BondFractions|Ba - Os bond frac.', 'CoulombMatrix|coulomb matrix eig 235', 'BondFractions|Mn - Pu bond frac.', 'BondFractions|La - Si bond frac.', 'BondFractions|Ga - Yb bond frac.', 'BondFractions|Au - I bond frac.', 'BondFractions|C - Rh bond frac.', 'BondFractions|Fe - Ho bond frac.', 'BondFractions|Nb - Sm bond frac.', 'BondFractions|Co - Th bond frac.', 'BondFractions|Mg - Ru bond frac.', 'CoulombMatrix|coulomb matrix eig 109', 'SineCoulombMatrix|sine coulomb matrix eig 48', 'BondFractions|H - Sn bond frac.', 'BondFractions|Ga - Nb bond frac.', 'BondFractions|Nb - Zr bond frac.', 'BondFractions|Mo - N bond frac.', 'BondFractions|Ga - W bond frac.', 'BondFractions|Ac - Sm bond frac.', 'BondFractions|Ho - Sn bond frac.', 'BondFractions|Tb - Tl bond frac.', 'BondFractions|Ag - Zn bond frac.', 'BondFractions|Pd - Ru bond frac.', 'BondFractions|K - Tm bond frac.', 'BondFractions|Ag - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 45', 'BondFractions|Co - Cs bond frac.', 'BondFractions|Au - Ge bond frac.', 'CoulombMatrix|coulomb matrix eig 42', 'BondFractions|Si - Y bond frac.', 'BondFractions|N - Pt bond frac.', 'BondFractions|Ti - Zr bond frac.', 'BondFractions|I - Na bond frac.', 'BondFractions|Ac - Ge bond frac.', 'BondFractions|Bi - Th bond frac.', 'BondFractions|Eu - Sn bond frac.', 'BondFractions|Bi - Pa bond frac.', 'BondFractions|Pm - Rh bond frac.', 'BondFractions|Ge - Ni bond frac.', 'BondFractions|Cd - H bond frac.', 'BondFractions|Hg - U bond frac.', 'CoulombMatrix|coulomb matrix eig 280', 'SineCoulombMatrix|sine coulomb matrix eig 14', 'BondFractions|Bi - Pu bond frac.', 'BondFractions|Ho - I bond frac.', 'BondFractions|Ce - Nd bond frac.', 'BondFractions|Gd - Hf bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 234', 'BondFractions|Co - Eu bond frac.', 'BondFractions|K - Sn bond frac.', 'BondFractions|Bi - Zr bond frac.', 'BondFractions|Co - Pa bond frac.', 'BondFractions|P - Th bond frac.', 'BondFractions|Br - Se bond frac.', 'BondFractions|Cu - Ta bond frac.', 'BondFractions|Cs - S bond frac.', 'BondFractions|Mn - U bond frac.', 'BondFractions|Au - Ca bond frac.', 'BondFractions|Co - Lu bond frac.', 'BondFractions|Al - Sr bond frac.', 'BondFractions|Pd - Ta bond frac.', 'BondFractions|Np - Pd bond frac.', 'CoulombMatrix|coulomb matrix eig 157', 'BondFractions|Pb - Sr bond frac.', 'BondFractions|Mg - Nb bond frac.', 'BondFractions|Ac - Dy bond frac.', 'XRDPowderPattern|xrd_18', 'BondFractions|Ir - Te bond frac.', 'BondFractions|Ga - Li bond frac.', 'BondFractions|Th - V bond frac.', 'CoulombMatrix|coulomb matrix eig 209', 'SineCoulombMatrix|sine coulomb matrix eig 118', 'BondFractions|K - Sm bond frac.', 'BondFractions|P - Ru bond frac.', 'CoulombMatrix|coulomb matrix eig 107', 'BondFractions|Ac - Nd bond frac.', 'BondFractions|Eu - H bond frac.', 'BondFractions|Si - Tb bond frac.', 'BondFractions|Os - W bond frac.', 'BondFractions|As - La bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 105', 'BondFractions|Mo - V bond frac.', 'XRDPowderPattern|xrd_50', 'BondFractions|Al - As bond frac.', 'CoulombMatrix|coulomb matrix eig 267', 'BondFractions|Br - Xe bond frac.', 'BondFractions|Na - Tb bond frac.', 'BondFractions|Br - Cd bond frac.', 'BondFractions|N - Sr bond frac.', 'BondFractions|Dy - Yb bond frac.', 'BondFractions|Au - Sm bond frac.', 'BondFractions|Be - Cs bond frac.', 'BondFractions|Ru - Sm bond frac.', 'BondFractions|Ag - K bond frac.', 'BondFractions|F - Ir bond frac.', 'BondFractions|Cd - In bond frac.', 'BondFractions|Gd - I bond frac.', 'BondFractions|C - Pb bond frac.', 'BondFractions|Eu - In bond frac.', 'BondFractions|Cl - Nd bond frac.', 'BondFractions|H - Sc bond frac.', 'BondFractions|P - Rh bond frac.', 'BondFractions|Ge - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 217', 'BondFractions|Bi - Tl bond frac.', 'BondFractions|Lu - Te bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 35', 'BondFractions|Er - Pr bond frac.', 'BondFractions|Bi - Er bond frac.', 'CoulombMatrix|coulomb matrix eig 141', 'BondFractions|La - N bond frac.', 'BondFractions|Eu - Ti bond frac.', 'CoulombMatrix|coulomb matrix eig 19', 'BondFractions|Er - Si bond frac.', 'BondFractions|Ac - Te bond frac.', 'BondFractions|Sb - Se bond frac.', 'BondFractions|Ba - Eu bond frac.', 'BondFractions|As - Tb bond frac.', 'BondFractions|P - Sb bond frac.', 'BondFractions|Ba - Hf bond frac.', 'BondFractions|C - Nb bond frac.', 'BondFractions|Ga - Rb bond frac.', 'BondFractions|Mg - Os bond frac.', 'BondFractions|Ag - Cs bond frac.', 'BondFractions|Cd - U bond frac.', 'BondFractions|Sr - Zn bond frac.', 'BondFractions|As - Kr bond frac.', 'BondFractions|Hf - Na bond frac.', 'XRDPowderPattern|xrd_124', 'SineCoulombMatrix|sine coulomb matrix eig 255', 'BondFractions|Ge - Th bond frac.', 'BondFractions|Na - Pa bond frac.', 'BondFractions|Ge - V bond frac.', 'BondFractions|O - Pa bond frac.', 'BondFractions|Cd - Xe bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 125', 'BondFractions|Cs - Rh bond frac.', 'BondFractions|Sn - U bond frac.', 'CoulombMatrix|coulomb matrix eig 78', 'BondFractions|Au - Pr bond frac.', 'BondFractions|Hg - Mg bond frac.', 'CoulombMatrix|coulomb matrix eig 271', 'BondFractions|Ce - Co bond frac.', 'BondFractions|Nb - P bond frac.', 'BondFractions|P - Xe bond frac.', 'BondFractions|Mg - Y bond frac.', 'BondFractions|Pr - S bond frac.', 'BondFractions|Fe - Te bond frac.', 'BondFractions|Al - Cs bond frac.', 'BondFractions|Eu - Ir bond frac.', 'BondFractions|Ba - Sc bond frac.', 'BondFractions|Ba - Hg bond frac.', 'BondFractions|Fe - Tm bond frac.', 'CoulombMatrix|coulomb matrix eig 118', 'BondFractions|Mo - Pr bond frac.', 'BondFractions|Bi - Hf bond frac.', 'BondFractions|Mn - Nd bond frac.', 'CoulombMatrix|coulomb matrix eig 231', 'CoulombMatrix|coulomb matrix eig 92', 'BondFractions|Ag - Cr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 280', 'BondFractions|Pd - Sm bond frac.', 'BondFractions|La - W bond frac.', 'BondFractions|Mn - Yb bond frac.', 'XRDPowderPattern|xrd_74', 'BondFractions|Al - Lu bond frac.', 'BondFractions|F - Tm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 46', 'SineCoulombMatrix|sine coulomb matrix eig 137', 'CoulombMatrix|coulomb matrix eig 264', 'CoulombMatrix|coulomb matrix eig 150', 'BondFractions|Be - La bond frac.', 'BondFractions|Dy - Pd bond frac.', 'BondFractions|Xe - Zn bond frac.', 'BondFractions|N - Pm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 275', 'BondFractions|Bi - O bond frac.', 'BondFractions|Ir - Li bond frac.', 'BondFractions|As - P bond frac.', 'BondFractions|Sc - Sm bond frac.', 'BondFractions|Cr - Nb bond frac.', 'BondFractions|Ir - P bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 108', 'BondFractions|Ag - Ta bond frac.', 'BondFractions|Pu - Rb bond frac.', 'BondFractions|Br - La bond frac.', 'BondFractions|Cs - Gd bond frac.', 'BondFractions|Dy - Y bond frac.', 'BondFractions|Re - Zn bond frac.', 'CoulombMatrix|coulomb matrix eig 268', 'BondFractions|La - Mn bond frac.', 'BondFractions|Ba - Y bond frac.', 'XRDPowderPattern|xrd_78', 'BondFractions|Pa - Rh bond frac.', 'BondFractions|Os - Pa bond frac.', 'BondFractions|Cl - Se bond frac.', 'BondFractions|Ga - Pb bond frac.', 'BondFractions|Mn - Rb bond frac.', 'BondFractions|Eu - Pr bond frac.', 'CoulombMatrix|coulomb matrix eig 255', 'BondFractions|As - Ru bond frac.', 'BondFractions|Gd - Pd bond frac.', 'BondFractions|Co - Y bond frac.', 'BondFractions|Be - Hf bond frac.', 'BondFractions|In - V bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 130', 'BondFractions|Ce - H bond frac.', 'BondFractions|In - Rh bond frac.', 'BondFractions|I - Sr bond frac.', 'BondFractions|Ba - Cl bond frac.', 'BondFractions|C - In bond frac.', 'CoulombMatrix|coulomb matrix eig 73', 'CoulombMatrix|coulomb matrix eig 108', 'BondFractions|Sm - Te bond frac.', 'BondFractions|Ag - Gd bond frac.', 'BondFractions|Pd - Rh bond frac.', 'BondFractions|As - Nd bond frac.', 'BondFractions|Np - P bond frac.', 'BondFractions|Ba - Zn bond frac.', 'BondFractions|C - Gd bond frac.', 'BondFractions|Mo - Ru bond frac.', 'BondFractions|Os - Rb bond frac.', 'BondFractions|Ni - W bond frac.', 'BondFractions|I - Xe bond frac.', 'BondFractions|Pb - Si bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 38', 'SineCoulombMatrix|sine coulomb matrix eig 196', 'SineCoulombMatrix|sine coulomb matrix eig 165', 'BondFractions|F - Gd bond frac.', 'BondFractions|Gd - O bond frac.', 'BondFractions|Pr - Tb bond frac.', 'CoulombMatrix|coulomb matrix eig 236', 'BondFractions|Mo - Rh bond frac.', 'BondFractions|Br - Pb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 78', 'BondFractions|Ac - Pd bond frac.', 'BondFractions|Ce - Nb bond frac.', 'BondFractions|Cu - Os bond frac.', 'BondFractions|Er - P bond frac.', 'BondFractions|Pb - Rb bond frac.', 'BondFractions|Er - H bond frac.', 'BondFractions|Hg - Si bond frac.', 'BondFractions|Cu - Mo bond frac.', 'BondFractions|Cu - Pb bond frac.', 'BondFractions|Bi - Kr bond frac.', 'BondFractions|Cu - Te bond frac.', 'BondFractions|Cs - Hg bond frac.', 'CoulombMatrix|coulomb matrix eig 81', 'CoulombMatrix|coulomb matrix eig 198', 'BondFractions|Pa - Sb bond frac.', 'XRDPowderPattern|xrd_112', 'BondFractions|Ag - Rh bond frac.', 'BondFractions|S - Tm bond frac.', 'BondFractions|Hf - Ru bond frac.', 'BondFractions|Ca - Ru bond frac.', 'BondFractions|I - Zr bond frac.', 'BondFractions|Mg - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 205', 'BondFractions|Ba - Sb bond frac.', 'BondFractions|Ga - Tb bond frac.', 'BondFractions|Ag - Hf bond frac.', 'BondFractions|Ba - Se bond frac.', 'BondFractions|Ca - Y bond frac.', 'BondFractions|Er - Ga bond frac.', 'BondFractions|Ce - Pm bond frac.', 'BondFractions|Be - I bond frac.', 'BondFractions|Sb - Zn bond frac.', 'BondFractions|Pt - Pu bond frac.', 'BondFractions|Cr - Tm bond frac.', 'BondFractions|Ga - Mo bond frac.', 'BondFractions|B - Ru bond frac.', 'BondFractions|Ge - Tb bond frac.', 'BondFractions|Cl - Re bond frac.', 'BondFractions|Al - Pa bond frac.', 'BondFractions|La - La bond frac.', 'BondFractions|Ru - Se bond frac.', 'BondFractions|Au - Br bond frac.', 'BondFractions|As - Be bond frac.', 'BondFractions|Be - Eu bond frac.', 'BondFractions|Sc - Sr bond frac.', 'BondFractions|Cl - Sr bond frac.', 'BondFractions|C - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 252', 'BondFractions|Te - Zr bond frac.', 'BondFractions|Na - Nd bond frac.', 'BondFractions|Sc - Tm bond frac.', 'BondFractions|Bi - Ga bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 292', 'BondFractions|Br - Pu bond frac.', 'BondFractions|Cd - Pm bond frac.', 'BondFractions|Gd - Mn bond frac.', 'BondFractions|Ag - Ge bond frac.', 'BondFractions|O - Xe bond frac.', 'BondFractions|Sc - Zr bond frac.', 'BondFractions|Hf - Mg bond frac.', 'BondFractions|Br - Ta bond frac.', 'BondFractions|K - Mo bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 114', 'CoulombMatrix|coulomb matrix eig 147', 'BondFractions|Dy - Se bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 235', 'CoulombMatrix|coulomb matrix eig 69', 'BondFractions|Gd - Ta bond frac.', 'BondFractions|Sn - Th bond frac.', 'BondFractions|Re - Ta bond frac.', 'XRDPowderPattern|xrd_12', 'BondFractions|Hf - Nd bond frac.', 'BondFractions|Os - Th bond frac.', 'BondFractions|Cu - Rb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 217', 'SineCoulombMatrix|sine coulomb matrix eig 180', 'BondFractions|Hg - Tb bond frac.', 'BondFractions|As - K bond frac.', 'BondFractions|Er - Na bond frac.', 'BondFractions|Ni - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 57', 'BondFractions|Ru - Te bond frac.', 'BondFractions|Br - Si bond frac.', 'CoulombMatrix|coulomb matrix eig 142', 'BondFractions|I - Lu bond frac.', 'BondFractions|F - Yb bond frac.', 'BondFractions|Dy - Tl bond frac.', 'CoulombMatrix|coulomb matrix eig 152', 'CoulombMatrix|coulomb matrix eig 244', 'BondFractions|Na - Sm bond frac.', 'BondFractions|Sr - Te bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 203', 'BondFractions|Dy - La bond frac.', 'BondFractions|Ba - Tc bond frac.', 'BondFractions|Sn - Zr bond frac.', 'CoulombMatrix|coulomb matrix eig 180', 'BondFractions|Hg - Ti bond frac.', 'BondFractions|P - Sr bond frac.', 'BondFractions|Rb - Rh bond frac.', 'CoulombMatrix|coulomb matrix eig 29', 'BondFractions|Ho - Rh bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 176', 'BondFractions|Gd - In bond frac.', 'BondFractions|Cs - O bond frac.', 'BondFractions|Lu - Nb bond frac.', 'BondFractions|Cd - Tl bond frac.', 'BondFractions|N - Np bond frac.', 'BondFractions|B - Pm bond frac.', 'BondFractions|Re - Tm bond frac.', 'BondFractions|Tb - Y bond frac.', 'BondFractions|Li - Rh bond frac.', 'BondFractions|Nd - Th bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 60', 'BondFractions|Al - La bond frac.', 'BondFractions|Ir - Zr bond frac.', 'BondFractions|Br - Rb bond frac.', 'BondFractions|Co - U bond frac.', 'BondFractions|Li - Pu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 189', 'BondFractions|Dy - O bond frac.', 'BondFractions|Ge - In bond frac.', 'BondFractions|Rh - Sc bond frac.', 'BondFractions|Ce - Se bond frac.', 'BondFractions|Ba - H bond frac.', 'BondFractions|Ba - V bond frac.', 'BondFractions|As - Lu bond frac.', 'BondFractions|Al - W bond frac.', 'BondFractions|N - Pa bond frac.', 'BondFractions|Sn - V bond frac.', 'BondFractions|Cu - Gd bond frac.', 'BondFractions|Pr - Ti bond frac.', 'BondFractions|Nb - Rh bond frac.', 'BondFractions|Rh - Zn bond frac.', 'BondFractions|Ir - Pu bond frac.', 'BondFractions|Lu - Mg bond frac.', 'BondFractions|Ga - Tc bond frac.', 'BondFractions|Ac - Yb bond frac.', 'BondFractions|B - Cd bond frac.', 'BondFractions|Fe - Sn bond frac.', 'BondFractions|Lu - Tb bond frac.', 'BondFractions|Eu - Pa bond frac.', 'CoulombMatrix|coulomb matrix eig 216', 'BondFractions|As - Pr bond frac.', 'BondFractions|Yb - Zr bond frac.', 'CoulombMatrix|coulomb matrix eig 223', 'BondFractions|Sn - Te bond frac.', 'BondFractions|Cs - Fe bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 277', 'BondFractions|C - Hf bond frac.', 'CoulombMatrix|coulomb matrix eig 286', 'BondFractions|Er - Rh bond frac.', 'BondFractions|Bi - K bond frac.', 'BondFractions|Pd - Te bond frac.', 'BondFractions|Eu - Y bond frac.', 'BondFractions|Gd - Pa bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 92', 'BondFractions|I - Sb bond frac.', 'BondFractions|Pm - Sb bond frac.', 'CoulombMatrix|coulomb matrix eig 60', 'BondFractions|Cl - Gd bond frac.', 'BondFractions|Ba - C bond frac.', 'CoulombMatrix|coulomb matrix eig 274', 'BondFractions|Nb - U bond frac.', 'CoulombMatrix|coulomb matrix eig 192', 'BondFractions|Tm - Tm bond frac.', 'BondFractions|Eu - Re bond frac.', 'BondFractions|Ga - Gd bond frac.', 'BondFractions|Mo - Te bond frac.', 'BondFractions|Yb - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 144', 'BondFractions|Tb - Tm bond frac.', 'BondFractions|Bi - Sr bond frac.', 'BondFractions|La - Y bond frac.', 'CoulombMatrix|coulomb matrix eig 111', 'SineCoulombMatrix|sine coulomb matrix eig 177', 'BondFractions|Tl - W bond frac.', 'BondFractions|C - Se bond frac.', 'BondFractions|Be - Sn bond frac.', 'BondFractions|Pr - Rb bond frac.', 'BondFractions|In - Nd bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 153', 'BondFractions|Na - Ru bond frac.', 'BondFractions|Hg - Ta bond frac.', 'BondFractions|Pd - Zn bond frac.', 'BondFractions|Br - Tc bond frac.', 'BondFractions|Be - Br bond frac.', 'BondFractions|Er - In bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 257', 'BondFractions|H - Pm bond frac.', 'BondFractions|Er - Yb bond frac.', 'BondFractions|Mg - Pt bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 83', 'BondFractions|Cd - Gd bond frac.', 'BondFractions|W - Yb bond frac.', 'BondFractions|Ho - U bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 122', 'BondFractions|Ti - U bond frac.', 'BondFractions|As - Rh bond frac.', 'BondFractions|Rh - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 225', 'BondFractions|Hg - Ru bond frac.', 'BondFractions|Ag - C bond frac.', 'BondFractions|Ag - Sb bond frac.', 'BondFractions|I - Pu bond frac.', 'BondFractions|Ce - Sb bond frac.', 'BondFractions|Ag - N bond frac.', 'CoulombMatrix|coulomb matrix eig 97', 'BondFractions|Gd - Na bond frac.', 'BondFractions|K - Tb bond frac.', 'BondFractions|Bi - H bond frac.', 'CoulombMatrix|coulomb matrix eig 144', 'BondFractions|I - K bond frac.', 'BondFractions|O - Tl bond frac.', 'CoulombMatrix|coulomb matrix eig 203', 'SineCoulombMatrix|sine coulomb matrix eig 116', 'BondFractions|Pa - Pu bond frac.', 'BondFractions|Lu - Y bond frac.', 'BondFractions|S - Sb bond frac.', 'BondFractions|Al - Zr bond frac.', 'BondFractions|Tm - V bond frac.', 'BondFractions|Ga - Ni bond frac.', 'BondFractions|Pt - Ti bond frac.', 'BondFractions|Lu - V bond frac.', 'BondFractions|Al - Bi bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 45', 'BondFractions|Au - Cd bond frac.', 'BondFractions|Ni - Rb bond frac.', 'BondFractions|Ga - Th bond frac.', 'BondFractions|Ba - Np bond frac.', 'CoulombMatrix|coulomb matrix eig 134', 'BondFractions|Li - Tm bond frac.', 'BondFractions|Ga - Ho bond frac.', 'BondFractions|Li - Re bond frac.', 'BondFractions|Ta - Te bond frac.', 'BondFractions|Al - Hg bond frac.', 'BondFractions|As - Hg bond frac.', 'BondFractions|Nb - Se bond frac.', 'BondFractions|Ir - Mn bond frac.', 'BondFractions|Gd - Lu bond frac.', 'BondFractions|Ir - Os bond frac.', 'BondFractions|As - Yb bond frac.', 'BondFractions|F - Sb bond frac.', 'BondFractions|Li - Sb bond frac.', 'BondFractions|Ac - Th bond frac.', 'BondFractions|O - Sn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 267', 'BondFractions|Os - Sb bond frac.', 'BondFractions|Ac - Au bond frac.', 'BondFractions|Hf - Sb bond frac.', 'BondFractions|Ge - Zn bond frac.', 'CoulombMatrix|coulomb matrix eig 242', 'BondFractions|La - Nb bond frac.', 'XRDPowderPattern|xrd_24', 'BondFractions|Cs - Pu bond frac.', 'BondFractions|La - Sc bond frac.', 'BondFractions|Ac - N bond frac.', 'BondFractions|Cd - Ge bond frac.', 'BondFractions|Cr - Hf bond frac.', 'CoulombMatrix|coulomb matrix eig 93', 'SineCoulombMatrix|sine coulomb matrix eig 239', 'BondFractions|Hg - Zn bond frac.', 'BondFractions|Er - Nd bond frac.', 'BondFractions|Ru - Sb bond frac.', 'BondFractions|Pt - Y bond frac.', 'BondFractions|As - S bond frac.', 'BondFractions|In - Sn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 76', 'BondFractions|Hg - Ir bond frac.', 'BondFractions|Be - Rh bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 24', 'BondFractions|B - Np bond frac.', 'BondFractions|Eu - N bond frac.', 'BondFractions|Re - W bond frac.', 'BondFractions|Ca - Pu bond frac.', 'BondFractions|Er - Hg bond frac.', 'BondFractions|Hg - Sb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 104', 'BondFractions|Pr - Yb bond frac.', 'BondFractions|Dy - Mo bond frac.', 'BondFractions|Cd - Pt bond frac.', 'BondFractions|Np - Tl bond frac.', 'BondFractions|Te - Ti bond frac.', 'BondFractions|Al - Mo bond frac.', 'BondFractions|Be - Pt bond frac.', 'BondFractions|Hf - Te bond frac.', 'BondFractions|Pd - Rb bond frac.', 'CoulombMatrix|coulomb matrix eig 162', 'BondFractions|As - Ca bond frac.', 'BondFractions|Co - Rb bond frac.', 'BondFractions|Ag - Ru bond frac.', 'BondFractions|Rh - Sb bond frac.', 'XRDPowderPattern|xrd_22', 'BondFractions|Fe - W bond frac.', 'BondFractions|As - Te bond frac.', 'BondFractions|Br - Na bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 171', 'BondFractions|Au - Cr bond frac.', 'BondFractions|Dy - Ni bond frac.', 'BondFractions|Sb - Sm bond frac.', 'BondFractions|Cr - Yb bond frac.', 'BondFractions|Mo - Pu bond frac.', 'BondFractions|Ir - V bond frac.', 'BondFractions|Pr - Y bond frac.', 'CoulombMatrix|coulomb matrix eig 283', 'BondFractions|Cd - Y bond frac.', 'BondFractions|Ga - Ti bond frac.', 'BondFractions|Nb - Sr bond frac.', 'BondFractions|C - Lu bond frac.', 'BondFractions|As - Y bond frac.', 'BondFractions|P - Pa bond frac.', 'CoulombMatrix|coulomb matrix eig 293', 'BondFractions|Sm - Tl bond frac.', 'BondFractions|Hg - K bond frac.', 'BondFractions|Sc - U bond frac.', 'BondFractions|Be - Pr bond frac.', 'CoulombMatrix|coulomb matrix eig 37', 'BondFractions|Nb - Ta bond frac.', 'BondFractions|H - Tb bond frac.', 'BondFractions|Mo - Pt bond frac.', 'BondFractions|Cs - Mg bond frac.', 'BondFractions|Br - C bond frac.', 'BondFractions|Cu - Yb bond frac.', 'BondFractions|Sb - W bond frac.', 'BondFractions|Ar - Ar bond frac.', 'BondFractions|N - Tm bond frac.', 'BondFractions|Ba - Sr bond frac.', 'BondFractions|Ni - Yb bond frac.', 'BondFractions|Na - Pb bond frac.', 'BondFractions|Ho - Ru bond frac.', 'BondFractions|Ag - La bond frac.', 'BondFractions|Gd - Mo bond frac.', 'BondFractions|O - Y bond frac.', 'BondFractions|Ru - Tl bond frac.', 'BondFractions|Cd - Re bond frac.', 'BondFractions|Gd - Ir bond frac.', 'BondFractions|Gd - H bond frac.', 'BondFractions|La - Lu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 222', 'CoulombMatrix|coulomb matrix eig 218', 'BondFractions|Hg - Yb bond frac.', 'BondFractions|Cu - Tc bond frac.', 'BondFractions|Th - U bond frac.', 'BondFractions|Au - Ta bond frac.', 'BondFractions|Cr - Se bond frac.', 'BondFractions|Lu - Tl bond frac.', 'BondFractions|Dy - Sn bond frac.', 'BondFractions|C - Os bond frac.', 'BondFractions|N - Nb bond frac.', 'BondFractions|Hg - Tl bond frac.', 'BondFractions|Br - Cr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 123', 'BondFractions|Rh - Zr bond frac.', 'BondFractions|H - Ta bond frac.', 'BondFractions|Sc - Se bond frac.', 'BondFractions|Pu - Tc bond frac.', 'CoulombMatrix|coulomb matrix eig 261', 'CoulombMatrix|coulomb matrix eig 90', 'BondFractions|Al - Br bond frac.', 'BondFractions|Sn - Tl bond frac.', 'BondFractions|Mo - Nd bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 49', 'SineCoulombMatrix|sine coulomb matrix eig 236', 'XRDPowderPattern|xrd_28', 'BondFractions|Nb - Pa bond frac.', 'BondFractions|Mo - Sn bond frac.', 'BondFractions|Ce - N bond frac.', 'BondFractions|Be - Nb bond frac.', 'XRDPowderPattern|xrd_44', 'BondFractions|Ba - Te bond frac.', 'BondFractions|Pu - Sn bond frac.', 'BondFractions|Ni - Te bond frac.', 'BondFractions|C - W bond frac.', 'BondFractions|Pt - Zn bond frac.', 'BondFractions|Cd - Eu bond frac.', 'BondFractions|Hf - Ir bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 245', 'BondFractions|Re - Sc bond frac.', 'BondFractions|K - Rb bond frac.', 'BondFractions|Gd - Y bond frac.', 'BondFractions|Cd - La bond frac.', 'BondFractions|Gd - Th bond frac.', 'BondFractions|Os - Se bond frac.', 'BondFractions|Ba - Ge bond frac.', 'CoulombMatrix|coulomb matrix eig 58', 'CoulombMatrix|coulomb matrix eig 116', 'BondFractions|Cl - Tl bond frac.', 'BondFractions|Mo - Yb bond frac.', 'BondFractions|Ge - Sc bond frac.', 'BondFractions|Li - Y bond frac.', 'BondFractions|N - Os bond frac.', 'BondFractions|Pa - Ti bond frac.', 'BondFractions|As - Hf bond frac.', 'BondFractions|As - O bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 23', 'CoulombMatrix|coulomb matrix eig 137', 'CoulombMatrix|coulomb matrix eig 140', 'BondFractions|Ce - Hf bond frac.', 'BondFractions|B - W bond frac.', 'BondFractions|Gd - N bond frac.', 'BondFractions|Bi - Ru bond frac.', 'BondFractions|Mn - Mo bond frac.', 'BondFractions|Cu - Th bond frac.', 'BondFractions|B - Pt bond frac.', 'BondFractions|Cs - V bond frac.', 'BondFractions|Mo - U bond frac.', 'BondFractions|Fe - I bond frac.', 'BondFractions|Er - O bond frac.', 'BondFractions|N - Y bond frac.', 'BondFractions|Pm - Tb bond frac.', 'BondFractions|I - Rb bond frac.', 'BondFractions|Nd - Rh bond frac.', 'BondFractions|In - W bond frac.', 'BondFractions|Ge - K bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 287', 'BondFractions|Er - Pt bond frac.', 'BondFractions|Cs - Nd bond frac.', 'BondFractions|Co - Pd bond frac.', 'BondFractions|B - Nb bond frac.', 'BondFractions|Cl - Os bond frac.', 'BondFractions|Os - S bond frac.', 'BondFractions|Dy - Tm bond frac.', 'BondFractions|Se - W bond frac.', 'BondFractions|Br - Sn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 179', 'BondFractions|Au - Hg bond frac.', 'BondFractions|Dy - Mn bond frac.', 'BondFractions|Ac - Eu bond frac.', 'BondFractions|Ho - Ni bond frac.', 'BondFractions|Cl - Sm bond frac.', 'BondFractions|Dy - Pt bond frac.', 'BondFractions|Au - Pa bond frac.', 'BondFractions|Re - Sm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 270', 'SineCoulombMatrix|sine coulomb matrix eig 208', 'BondFractions|Hf - In bond frac.', 'BondFractions|S - Yb bond frac.', 'BondFractions|S - Sm bond frac.', 'BondFractions|Fe - Pt bond frac.', 'BondFractions|H - Xe bond frac.', 'BondFractions|H - Sr bond frac.', 'BondFractions|Ge - Te bond frac.', 'BondFractions|Cr - U bond frac.', 'BondFractions|Ga - Sb bond frac.', 'BondFractions|Ga - N bond frac.', 'BondFractions|Hf - Hg bond frac.', 'BondFractions|Eu - I bond frac.', 'BondFractions|Tc - Xe bond frac.', 'BondFractions|Cd - Nb bond frac.', 'BondFractions|Hf - Yb bond frac.', 'BondFractions|Mo - Tl bond frac.', 'BondFractions|Ga - Pr bond frac.', 'XRDPowderPattern|xrd_4', 'BondFractions|Np - S bond frac.', 'BondFractions|La - P bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 191', 'BondFractions|P - Pu bond frac.', 'BondFractions|Ga - Pd bond frac.', 'BondFractions|Cs - W bond frac.', 'BondFractions|Br - Ce bond frac.', 'BondFractions|C - Th bond frac.', 'BondFractions|Ba - Ti bond frac.', 'BondFractions|Hg - Nd bond frac.', 'BondFractions|Ag - Tm bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 107', 'BondFractions|Ru - Si bond frac.', 'BondFractions|Cd - Pu bond frac.', 'BondFractions|Ni - Tm bond frac.', 'BondFractions|Tl - Zr bond frac.', 'BondFractions|Br - Ca bond frac.', 'BondFractions|Cd - Co bond frac.', 'BondFractions|B - Mo bond frac.', 'BondFractions|Pt - Sm bond frac.', 'BondFractions|Hf - U bond frac.', 'BondFractions|As - Fe bond frac.', 'BondFractions|Cu - Hf bond frac.', 'BondFractions|Ge - O bond frac.', 'BondFractions|Dy - Hg bond frac.', 'BondFractions|Nd - Ni bond frac.', 'BondFractions|Ba - Nd bond frac.', 'BondFractions|Ir - Pr bond frac.', 'BondFractions|Br - Co bond frac.', 'BondFractions|Cd - Cr bond frac.', 'BondFractions|Br - Pa bond frac.', 'BondFractions|Os - Pu bond frac.', 'BondFractions|Fe - Os bond frac.', 'BondFractions|Cs - Sm bond frac.', 'BondFractions|Cu - U bond frac.', 'BondFractions|Ru - Tc bond frac.', 'BondFractions|O - Pr bond frac.', 'CoulombMatrix|coulomb matrix eig 40', 'CoulombMatrix|coulomb matrix eig 88', 'BondFractions|Cd - F bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 209', 'BondFractions|Be - Tc bond frac.', 'BondFractions|Dy - Pm bond frac.', 'CoulombMatrix|coulomb matrix eig 51', 'BondFractions|Al - Th bond frac.', 'BondFractions|Hf - Sc bond frac.', 'BondFractions|Ba - Tl bond frac.', 'BondFractions|Cs - Lu bond frac.', 'BondFractions|Ac - Na bond frac.', 'BondFractions|O - Pd bond frac.', 'BondFractions|Ca - Rb bond frac.', 'CoulombMatrix|coulomb matrix eig 130', 'BondFractions|Br - Sm bond frac.', 'BondFractions|Pa - Pt bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 62', 'SineCoulombMatrix|sine coulomb matrix eig 185', 'BondFractions|Al - I bond frac.', 'BondFractions|Gd - Ti bond frac.', 'BondFractions|Cs - U bond frac.', 'BondFractions|N - Th bond frac.', 'BondFractions|Ag - Re bond frac.', 'BondFractions|C - Tm bond frac.', 'BondFractions|Ce - Si bond frac.', 'BondFractions|Th - Yb bond frac.', 'BondFractions|Au - Tl bond frac.', 'XRDPowderPattern|xrd_64', 'BondFractions|H - K bond frac.', 'BondFractions|Eu - Se bond frac.', 'BondFractions|Lu - Nd bond frac.', 'BondFractions|Ge - Ge bond frac.', 'BondFractions|Nb - Sb bond frac.', 'BondFractions|B - Nd bond frac.', 'BondFractions|Co - Yb bond frac.', 'BondFractions|Mo - Zn bond frac.', 'BondFractions|Cs - Ho bond frac.', 'BondFractions|Sn - Ti bond frac.', 'BondFractions|S - Tb bond frac.', 'BondFractions|Cr - Eu bond frac.', 'BondFractions|K - W bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 132', 'BondFractions|Pr - Ta bond frac.', 'BondFractions|Bi - Cd bond frac.', 'BondFractions|Tb - U bond frac.', 'BondFractions|Si - Sr bond frac.', 'BondFractions|Bi - Nb bond frac.', 'BondFractions|Mn - Pt bond frac.', 'BondFractions|Hf - Pb bond frac.', 'BondFractions|Pb - Pm bond frac.', 'CoulombMatrix|coulomb matrix eig 277', 'BondFractions|Bi - W bond frac.', 'BondFractions|Pm - Sc bond frac.', 'BondFractions|As - Pd bond frac.', 'BondFractions|Au - Pb bond frac.', 'BondFractions|W - Zn bond frac.', 'BondFractions|Be - Pd bond frac.', 'BondFractions|Au - Nd bond frac.', 'CoulombMatrix|coulomb matrix eig 126', 'BondFractions|I - Pd bond frac.', 'BondFractions|F - Hg bond frac.', 'BondFractions|Ir - Pb bond frac.', 'BondFractions|I - Ni bond frac.', 'CoulombMatrix|coulomb matrix eig 135', 'BondFractions|Lu - Mo bond frac.', 'BondFractions|Hf - Ta bond frac.', 'BondFractions|Tl - U bond frac.', 'BondFractions|Hg - W bond frac.', 'BondFractions|C - Te bond frac.', 'CoulombMatrix|coulomb matrix eig 20', 'BondFractions|Cr - Ru bond frac.', 'BondFractions|Mg - Sm bond frac.', 'BondFractions|Ir - Nb bond frac.', 'BondFractions|Ba - Sn bond frac.', 'BondFractions|Cd - Cs bond frac.', 'BondFractions|H - V bond frac.', 'BondFractions|Ac - Sb bond frac.', 'BondFractions|Be - Np bond frac.', 'BondFractions|Lu - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 104', 'BondFractions|Au - Bi bond frac.', 'BondFractions|Th - Zr bond frac.', 'BondFractions|Al - Rb bond frac.', 'BondFractions|Ga - Te bond frac.', 'BondFractions|Mg - Sn bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 265', 'BondFractions|Tc - Tc bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 39', 'CoulombMatrix|coulomb matrix eig 122', 'BondFractions|K - Pt bond frac.', 'BondFractions|Mg - W bond frac.', 'BondFractions|Pr - Rh bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 293', 'CoulombMatrix|coulomb matrix eig 179', 'BondFractions|B - In bond frac.', 'BondFractions|Pa - Sn bond frac.', 'BondFractions|H - Li bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 91', 'BondFractions|Au - Si bond frac.', 'BondFractions|Ac - Cs bond frac.', 'CoulombMatrix|coulomb matrix eig 188', 'BondFractions|As - Ce bond frac.', 'BondFractions|Bi - Fe bond frac.', 'BondFractions|Ge - Ir bond frac.', 'BondFractions|Dy - Nd bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 119', 'BondFractions|Ba - Nb bond frac.', 'BondFractions|Ga - Mn bond frac.', 'BondFractions|Br - Hg bond frac.', 'BondFractions|Fe - Yb bond frac.', 'BondFractions|Cr - H bond frac.', 'BondFractions|Ho - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 129', 'SineCoulombMatrix|sine coulomb matrix eig 53', 'XRDPowderPattern|xrd_16', 'BondFractions|H - Ru bond frac.', 'BondFractions|Ho - Pb bond frac.', 'BondFractions|C - Tb bond frac.', 'BondFractions|Ca - Re bond frac.', 'BondFractions|Co - Ta bond frac.', 'BondFractions|Np - Rh bond frac.', 'BondFractions|Bi - F bond frac.', 'BondFractions|Er - Mg bond frac.', 'BondFractions|Ir - Zn bond frac.', 'BondFractions|Er - Sb bond frac.', 'BondFractions|Si - W bond frac.', 'BondFractions|I - N bond frac.', 'BondFractions|Ac - Tl bond frac.', 'BondFractions|Au - Sb bond frac.', 'BondFractions|Co - Pr bond frac.', 'BondFractions|Tm - Zr bond frac.', 'BondFractions|Mo - Ti bond frac.', 'BondFractions|Nd - W bond frac.', 'BondFractions|Sm - Ti bond frac.', 'BondFractions|Er - Te bond frac.', 'BondFractions|C - Np bond frac.', 'BondFractions|Ir - Lu bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 162', 'BondFractions|Ce - I bond frac.', 'BondFractions|Ac - In bond frac.', 'BondFractions|K - Pd bond frac.', 'BondFractions|Ca - Ho bond frac.', 'BondFractions|Sb - Zr bond frac.', 'BondFractions|As - N bond frac.', 'BondFractions|Cu - Ge bond frac.', 'CoulombMatrix|coulomb matrix eig 158', 'BondFractions|Ce - In bond frac.', 'BondFractions|Al - Te bond frac.', 'BondFractions|Pb - Xe bond frac.', 'BondFractions|Br - Ho bond frac.', 'BondFractions|Ni - Np bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 207', 'BondFractions|Fe - Ga bond frac.', 'BondFractions|Er - V bond frac.', 'BondFractions|Hg - Se bond frac.', 'BondFractions|Te - V bond frac.', 'BondFractions|Os - Tl bond frac.', 'CoulombMatrix|coulomb matrix eig 47', 'CoulombMatrix|coulomb matrix eig 229', 'BondFractions|Ac - Er bond frac.', 'BondFractions|Ni - Y bond frac.', 'BondFractions|Cr - Nd bond frac.', 'BondFractions|Sb - Tb bond frac.', 'BondFractions|Bi - Pm bond frac.', 'BondFractions|Pb - Pd bond frac.', 'BondFractions|Nb - W bond frac.', 'BondFractions|Br - Gd bond frac.', 'BondFractions|Dy - Ho bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 190', 'BondFractions|Ge - Li bond frac.', 'BondFractions|Br - Tm bond frac.', 'BondFractions|Ga - P bond frac.', 'BondFractions|Dy - S bond frac.', 'CoulombMatrix|coulomb matrix eig 138', 'XRDPowderPattern|xrd_80', 'BondFractions|Eu - Hf bond frac.', 'BondFractions|P - Re bond frac.', 'BondFractions|Bi - V bond frac.', 'BondFractions|Bi - Se bond frac.', 'CoulombMatrix|coulomb matrix eig 74', 'BondFractions|Rb - Sb bond frac.', 'BondFractions|Os - V bond frac.', 'BondFractions|Ca - Mo bond frac.', 'CoulombMatrix|coulomb matrix eig 89', 'BondFractions|Ge - Mg bond frac.', 'BondFractions|Er - W bond frac.', 'BondFractions|B - Pu bond frac.', 'BondFractions|Ir - Pa bond frac.', 'BondFractions|Bi - Ta bond frac.', 'BondFractions|S - Se bond frac.', 'BondFractions|Sm - Yb bond frac.', 'BondFractions|Br - Re bond frac.', 'BondFractions|Ga - Lu bond frac.', 'CoulombMatrix|coulomb matrix eig 196', 'BondFractions|B - Bi bond frac.', 'BondFractions|Ce - Ga bond frac.', 'BondFractions|N - Xe bond frac.', 'BondFractions|Fe - Pa bond frac.', 'BondFractions|Mo - Y bond frac.', 'BondFractions|Sc - Yb bond frac.', 'BondFractions|Ir - Sm bond frac.', 'BondFractions|Nb - Tm bond frac.', 'BondFractions|F - Pr bond frac.', 'BondFractions|Pd - Zr bond frac.', 'BondFractions|Be - Pu bond frac.', 'BondFractions|Ba - Er bond frac.', 'BondFractions|Tm - W bond frac.', 'BondFractions|Ba - Pa bond frac.', 'BondFractions|Dy - Mg bond frac.', 'BondFractions|Lu - Si bond frac.', 'BondFractions|In - Zn bond frac.', 'BondFractions|Al - Pb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 13', 'BondFractions|As - Sc bond frac.', 'BondFractions|I - Ta bond frac.', 'BondFractions|Dy - F bond frac.', 'CoulombMatrix|coulomb matrix eig 36', 'BondFractions|Ge - W bond frac.', 'BondFractions|Re - Th bond frac.', 'BondFractions|I - Sm bond frac.', 'BondFractions|Br - Rh bond frac.', 'BondFractions|O - Pm bond frac.', 'BondFractions|Ta - Y bond frac.', 'BondFractions|Hf - K bond frac.', 'BondFractions|Ce - Sm bond frac.', 'BondFractions|Pm - S bond frac.', 'BondFractions|Rb - Th bond frac.', 'BondFractions|U - Zr bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 194', 'BondFractions|Br - H bond frac.', 'BondFractions|Os - Zn bond frac.', 'BondFractions|La - Rb bond frac.', 'CoulombMatrix|coulomb matrix eig 75', 'SineCoulombMatrix|sine coulomb matrix eig 64', 'BondFractions|Ir - N bond frac.', 'BondFractions|Pa - Pd bond frac.', 'BondFractions|H - Hf bond frac.', 'BondFractions|Dy - Tc bond frac.', 'BondFractions|Sb - Tl bond frac.', 'BondFractions|Er - Zr bond frac.', 'CoulombMatrix|coulomb matrix eig 287', 'BondFractions|Er - Pb bond frac.', 'CoulombMatrix|coulomb matrix eig 197', 'BondFractions|Ca - Se bond frac.', 'BondFractions|Rh - Ta bond frac.', 'BondFractions|Mg - Tb bond frac.', 'BondFractions|Pu - Ru bond frac.', 'BondFractions|Nb - Ru bond frac.', 'BondFractions|Ag - Sn bond frac.', 'CoulombMatrix|coulomb matrix eig 167', 'BondFractions|As - F bond frac.', 'BondFractions|Ce - Re bond frac.', 'CoulombMatrix|coulomb matrix eig 62', 'BondFractions|Cr - La bond frac.', 'BondFractions|Tl - Yb bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 84', 'BondFractions|Be - Y bond frac.', 'BondFractions|In - Pu bond frac.', 'BondFractions|Er - Th bond frac.', 'BondFractions|N - Rh bond frac.', 'CoulombMatrix|coulomb matrix eig 117', 'BondFractions|Cs - Tc bond frac.', 'XRDPowderPattern|xrd_100', 'BondFractions|Si - Sn bond frac.', 'CoulombMatrix|coulomb matrix eig 164', 'BondFractions|Ir - Th bond frac.', 'BondFractions|Ac - Sc bond frac.', 'BondFractions|Na - Np bond frac.', 'BondFractions|Mg - Ta bond frac.', 'BondFractions|Cd - Er bond frac.', 'BondFractions|Pt - V bond frac.', 'BondFractions|Hg - Ho bond frac.', 'BondFractions|Er - Re bond frac.', 'BondFractions|K - Tc bond frac.', 'BondFractions|C - Er bond frac.', 'BondFractions|Se - Se bond frac.', 'BondFractions|Ga - Sr bond frac.', 'BondFractions|Cu - Sn bond frac.', 'BondFractions|Ir - Tc bond frac.', 'BondFractions|Gd - Zr bond frac.', 'BondFractions|Co - Te bond frac.', 'BondFractions|Pb - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 270', 'BondFractions|Au - Se bond frac.', 'BondFractions|Dy - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 13', 'BondFractions|La - Yb bond frac.', 'BondFractions|N - Sb bond frac.', 'BondFractions|Mn - W bond frac.', 'CoulombMatrix|coulomb matrix eig 113', 'BondFractions|Mn - Os bond frac.', 'BondFractions|Ta - Tm bond frac.', 'BondFractions|Ce - S bond frac.', 'BondFractions|Sc - Te bond frac.', 'BondFractions|Ag - Eu bond frac.', 'CoulombMatrix|coulomb matrix eig 220', 'BondFractions|Cu - Tl bond frac.', 'BondFractions|Eu - Te bond frac.', 'BondFractions|N - Pr bond frac.', 'BondFractions|Au - Rb bond frac.', 'BondFractions|Cs - Ni bond frac.', 'BondFractions|Pr - Zn bond frac.', 'BondFractions|Ag - Be bond frac.', 'BondFractions|Eu - Si bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 148', 'BondFractions|Au - F bond frac.', 'BondFractions|Pb - Re bond frac.', 'BondFractions|P - Sm bond frac.', 'BondFractions|I - Y bond frac.', 'BondFractions|Fe - Hg bond frac.', 'BondFractions|F - Sm bond frac.', 'CoulombMatrix|coulomb matrix eig 101', 'BondFractions|H - Pu bond frac.', 'BondFractions|Pr - Se bond frac.', 'BondFractions|Pt - Rb bond frac.', 'CoulombMatrix|coulomb matrix eig 91', 'BondFractions|Li - Sn bond frac.', 'CoulombMatrix|coulomb matrix eig 59', 'BondFractions|Cd - Li bond frac.', 'BondFractions|As - Sm bond frac.', 'BondFractions|Mo - Pd bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 77', 'BondFractions|H - La bond frac.', 'BondFractions|Tm - U bond frac.', 'BondFractions|Ho - Ta bond frac.', 'BondFractions|Ac - Ni bond frac.', 'BondFractions|Pm - Tm bond frac.', 'BondFractions|Pa - Rb bond frac.', 'BondFractions|Cl - Rh bond frac.', 'SineCoulombMatrix|sine coulomb matrix eig 140', 'SineCoulombMatrix|sine coulomb matrix eig 31', 'SineCoulombMatrix|sine coulomb matrix eig 156', 'BondFractions|I - S bond frac.', 'BondFractions|K - Se bond frac.', 'BondFractions|Ce - Ru bond frac.', 'BondFractions|Ca - Tl bond frac.'}\n",
+      "2021-02-24 14:27:53,446 - modnet - INFO - Starting target 1/1: refractive_index ...\n",
+      "2021-02-24 14:27:53,450 - modnet - INFO - Computing mutual information between features and target...\n",
+      "2021-02-24 14:32:37,844 - modnet - INFO - Computing optimal features...\n",
+      "2021-02-24 14:32:59,177 - modnet - INFO - Selected 50/1019 features...\n",
+      "2021-02-24 14:33:20,065 - modnet - INFO - Selected 100/1019 features...\n",
+      "2021-02-24 14:33:40,132 - modnet - INFO - Selected 150/1019 features...\n",
+      "2021-02-24 14:33:59,150 - modnet - INFO - Selected 200/1019 features...\n",
+      "2021-02-24 14:34:17,283 - modnet - INFO - Selected 250/1019 features...\n",
+      "2021-02-24 14:34:34,455 - modnet - INFO - Selected 300/1019 features...\n",
+      "2021-02-24 14:34:50,608 - modnet - INFO - Selected 350/1019 features...\n",
+      "2021-02-24 14:35:05,670 - modnet - INFO - Selected 400/1019 features...\n",
+      "2021-02-24 14:35:19,783 - modnet - INFO - Selected 450/1019 features...\n",
+      "2021-02-24 14:35:32,651 - modnet - INFO - Selected 500/1019 features...\n",
+      "2021-02-24 14:35:44,470 - modnet - INFO - Selected 550/1019 features...\n",
+      "2021-02-24 14:35:55,102 - modnet - INFO - Selected 600/1019 features...\n",
+      "2021-02-24 14:36:04,730 - modnet - INFO - Selected 650/1019 features...\n",
+      "2021-02-24 14:36:13,076 - modnet - INFO - Selected 700/1019 features...\n",
+      "2021-02-24 14:36:20,379 - modnet - INFO - Selected 750/1019 features...\n",
+      "2021-02-24 14:36:26,467 - modnet - INFO - Selected 800/1019 features...\n",
+      "2021-02-24 14:36:31,319 - modnet - INFO - Selected 850/1019 features...\n",
+      "2021-02-24 14:36:34,927 - modnet - INFO - Selected 900/1019 features...\n",
+      "2021-02-24 14:36:37,383 - modnet - INFO - Selected 950/1019 features...\n",
+      "2021-02-24 14:36:38,637 - modnet - INFO - Selected 1000/1019 features...\n",
+      "2021-02-24 14:36:38,804 - modnet - INFO - Done with target 1/1: refractive_index.\n",
+      "2021-02-24 14:36:38,804 - modnet - INFO - Merging all features...\n",
+      "2021-02-24 14:36:38,807 - modnet - INFO - Done.\n"
      ]
     }
    ],
    "source": [
-    "md.feature_selection(n=1100)"
+    "md.feature_selection(n=-1,\n",
+    "                     use_precomputed_cross_nmi=True\n",
+    "                    ) # Here we use precomputed cross_nmi to save time"
    ]
   },
   {
@@ -610,10 +615,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:root:Data successfully saved as out/md_ref_index!\n"
+      "2021-02-24 14:36:42,692 - modnet - INFO - Data successfully saved as out/md_ref_index!\n"
      ]
     }
    ],
@@ -634,10 +639,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:root:Loaded <modnet.preprocessing.MODData object at 0x7fe2c411a8b0> object, created with modnet version 0.1.8\n"
+      "2021-02-24 14:36:44,826 - modnet - INFO - Loaded <modnet.preprocessing.MODData object at 0x7ffae983dcd0> object, created with modnet version 0.1.9~develop\n"
      ]
     }
    ],
@@ -683,7 +688,11 @@
     }
    ],
    "source": [
-    "model = MODNetModel([[['refractive_index']]],{'refractive_index':1},n_feat=1000, num_neurons=[[128],[64],[32],[]], act='elu')\n",
+    "model = MODNetModel([[['refractive_index']]],{'refractive_index':1},\n",
+    "                    n_feat=1000,\n",
+    "                    num_neurons=[[128],[64],[32],[]],\n",
+    "                    act='elu'\n",
+    "                   )\n",
     "model.model.summary()"
    ]
   },
@@ -694,1257 +703,893 @@
     "### (b) Training the model"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### option 1: using the fit_preset function"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#model.fit_preset(md,nested=0) # no innner CV is used (only simple train-val here)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### option 2: using the fit function\n",
+    "In this case, the user provides hand-chosen hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = MODNetModel([[['refractive_index']]],\n",
+    "                    {'refractive_index':1},\n",
+    "                    n_feat=1000,\n",
+    "                    num_neurons=[[128],[64],[32],[]],\n",
+    "                    act='elu'\n",
+    "                   )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Compiling model...\n",
-      "INFO:root:Fitting model...\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Epoch 1/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.2901 - mae: 0.2901epoch 0: loss: 0.258, val_loss:0.144 val_mae:0.144\n",
-      "57/57 [==============================] - 0s 3ms/step - loss: 0.2584 - mae: 0.2584 - val_loss: 0.1436 - val_mae: 0.1436\n",
+      "57/57 [==============================] - 0s 3ms/step - loss: 0.3439 - mae: 0.3439 - val_loss: 0.1677 - val_mae: 0.1677\n",
       "Epoch 2/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.1382 - mae: 0.1382epoch 1: loss: 0.138, val_loss:0.126 val_mae:0.126\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.1385 - mae: 0.1385 - val_loss: 0.1257 - val_mae: 0.1257\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.1551 - mae: 0.1551 - val_loss: 0.1283 - val_mae: 0.1283\n",
       "Epoch 3/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.1225 - mae: 0.1225epoch 2: loss: 0.124, val_loss:0.124 val_mae:0.124\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.1241 - mae: 0.1241 - val_loss: 0.1244 - val_mae: 0.1244\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.1377 - mae: 0.1377 - val_loss: 0.1145 - val_mae: 0.1145\n",
       "Epoch 4/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.1173 - mae: 0.1173epoch 3: loss: 0.116, val_loss:0.104 val_mae:0.104\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.1155 - mae: 0.1155 - val_loss: 0.1041 - val_mae: 0.1041\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.1212 - mae: 0.1212 - val_loss: 0.1055 - val_mae: 0.1055\n",
       "Epoch 5/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.1090 - mae: 0.1090epoch 4: loss: 0.108, val_loss:0.106 val_mae:0.106\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.1082 - mae: 0.1082 - val_loss: 0.1062 - val_mae: 0.1062\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.1041 - mae: 0.1041 - val_loss: 0.1026 - val_mae: 0.1026\n",
       "Epoch 6/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0984 - mae: 0.0984epoch 5: loss: 0.096, val_loss:0.100 val_mae:0.100\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0960 - mae: 0.0960 - val_loss: 0.1002 - val_mae: 0.1002\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.1143 - mae: 0.1143 - val_loss: 0.1047 - val_mae: 0.1047\n",
       "Epoch 7/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.1052 - mae: 0.1052epoch 6: loss: 0.105, val_loss:0.096 val_mae:0.096\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.1055 - mae: 0.1055 - val_loss: 0.0959 - val_mae: 0.0959\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0976 - mae: 0.0976 - val_loss: 0.1191 - val_mae: 0.1191\n",
       "Epoch 8/300\n",
-      "47/57 [=======================>......] - ETA: 0s - loss: 0.0948 - mae: 0.0948epoch 7: loss: 0.094, val_loss:0.114 val_mae:0.114\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0941 - mae: 0.0941 - val_loss: 0.1140 - val_mae: 0.1140\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.1116 - mae: 0.1116 - val_loss: 0.1286 - val_mae: 0.1286\n",
       "Epoch 9/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0912 - mae: 0.0912epoch 8: loss: 0.093, val_loss:0.088 val_mae:0.088\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0935 - mae: 0.0935 - val_loss: 0.0882 - val_mae: 0.0882\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.1184 - mae: 0.1184 - val_loss: 0.0920 - val_mae: 0.0920\n",
       "Epoch 10/300\n",
-      "48/57 [========================>.....] - ETA: 0s - loss: 0.0822 - mae: 0.0822epoch 9: loss: 0.084, val_loss:0.102 val_mae:0.102\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0838 - mae: 0.0838 - val_loss: 0.1018 - val_mae: 0.1018\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0888 - mae: 0.0888 - val_loss: 0.0924 - val_mae: 0.0924\n",
       "Epoch 11/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0823 - mae: 0.0823epoch 10: loss: 0.082, val_loss:0.084 val_mae:0.084\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0821 - mae: 0.0821 - val_loss: 0.0837 - val_mae: 0.0837\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0926 - mae: 0.0926 - val_loss: 0.1028 - val_mae: 0.1028\n",
       "Epoch 12/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0831 - mae: 0.0831epoch 11: loss: 0.082, val_loss:0.083 val_mae:0.083\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0816 - mae: 0.0816 - val_loss: 0.0826 - val_mae: 0.0826\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0869 - mae: 0.0869 - val_loss: 0.0898 - val_mae: 0.0898\n",
       "Epoch 13/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0724 - mae: 0.0724epoch 12: loss: 0.076, val_loss:0.152 val_mae:0.152\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0755 - mae: 0.0755 - val_loss: 0.1517 - val_mae: 0.1517\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0813 - mae: 0.0813 - val_loss: 0.0909 - val_mae: 0.0909\n",
       "Epoch 14/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0924 - mae: 0.0924epoch 13: loss: 0.090, val_loss:0.089 val_mae:0.089\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0896 - mae: 0.0896 - val_loss: 0.0892 - val_mae: 0.0892\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0817 - mae: 0.0817 - val_loss: 0.0839 - val_mae: 0.0839\n",
       "Epoch 15/300\n",
-      "50/57 [=========================>....] - ETA: 0s - loss: 0.0756 - mae: 0.0756epoch 14: loss: 0.076, val_loss:0.090 val_mae:0.090\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0761 - mae: 0.0761 - val_loss: 0.0896 - val_mae: 0.0896\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0799 - mae: 0.0799 - val_loss: 0.0855 - val_mae: 0.0855\n",
       "Epoch 16/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0742 - mae: 0.0742epoch 15: loss: 0.074, val_loss:0.089 val_mae:0.089\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0744 - mae: 0.0744 - val_loss: 0.0891 - val_mae: 0.0891\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0783 - mae: 0.0783 - val_loss: 0.0864 - val_mae: 0.0864\n",
       "Epoch 17/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0776 - mae: 0.0776epoch 16: loss: 0.077, val_loss:0.087 val_mae:0.087\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0773 - mae: 0.0773 - val_loss: 0.0869 - val_mae: 0.0869\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0831 - mae: 0.0831 - val_loss: 0.0952 - val_mae: 0.0952\n",
       "Epoch 18/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0727 - mae: 0.0727epoch 17: loss: 0.073, val_loss:0.074 val_mae:0.074\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0732 - mae: 0.0732 - val_loss: 0.0735 - val_mae: 0.0735\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0732 - mae: 0.0732 - val_loss: 0.0789 - val_mae: 0.0789\n",
       "Epoch 19/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0684 - mae: 0.0684epoch 18: loss: 0.072, val_loss:0.131 val_mae:0.131\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0723 - mae: 0.0723 - val_loss: 0.1312 - val_mae: 0.1312\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0728 - mae: 0.0728 - val_loss: 0.0830 - val_mae: 0.0830\n",
       "Epoch 20/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0957 - mae: 0.0957epoch 19: loss: 0.087, val_loss:0.077 val_mae:0.077\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0874 - mae: 0.0874 - val_loss: 0.0769 - val_mae: 0.0769\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0769 - mae: 0.0769 - val_loss: 0.0866 - val_mae: 0.0866\n",
       "Epoch 21/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0666 - mae: 0.0666epoch 20: loss: 0.066, val_loss:0.083 val_mae:0.083\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0665 - mae: 0.0665 - val_loss: 0.0829 - val_mae: 0.0829\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0873 - mae: 0.0873 - val_loss: 0.1037 - val_mae: 0.1037\n",
       "Epoch 22/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0705 - mae: 0.0705epoch 21: loss: 0.070, val_loss:0.077 val_mae:0.077\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0703 - mae: 0.0703 - val_loss: 0.0769 - val_mae: 0.0769\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0718 - mae: 0.0718 - val_loss: 0.0832 - val_mae: 0.0832\n",
       "Epoch 23/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0709 - mae: 0.0709epoch 22: loss: 0.074, val_loss:0.083 val_mae:0.083\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0739 - mae: 0.0739 - val_loss: 0.0830 - val_mae: 0.0830\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0662 - mae: 0.0662 - val_loss: 0.0753 - val_mae: 0.0753\n",
       "Epoch 24/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0715 - mae: 0.0715epoch 23: loss: 0.069, val_loss:0.072 val_mae:0.072\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0688 - mae: 0.0688 - val_loss: 0.0718 - val_mae: 0.0718\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0719 - mae: 0.0719 - val_loss: 0.0827 - val_mae: 0.0827\n",
       "Epoch 25/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0630 - mae: 0.0630epoch 24: loss: 0.065, val_loss:0.079 val_mae:0.079\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0649 - mae: 0.0649 - val_loss: 0.0794 - val_mae: 0.0794\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0689 - mae: 0.0689 - val_loss: 0.0929 - val_mae: 0.0929\n",
       "Epoch 26/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0646 - mae: 0.0646epoch 25: loss: 0.067, val_loss:0.078 val_mae:0.078\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0666 - mae: 0.0666 - val_loss: 0.0777 - val_mae: 0.0777\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0651 - mae: 0.0651 - val_loss: 0.0833 - val_mae: 0.0833\n",
       "Epoch 27/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0628 - mae: 0.0628epoch 26: loss: 0.065, val_loss:0.105 val_mae:0.105\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0646 - mae: 0.0646 - val_loss: 0.1047 - val_mae: 0.1047\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0654 - mae: 0.0654 - val_loss: 0.0769 - val_mae: 0.0769\n",
       "Epoch 28/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0690 - mae: 0.0690epoch 27: loss: 0.067, val_loss:0.075 val_mae:0.075\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0671 - mae: 0.0671 - val_loss: 0.0746 - val_mae: 0.0746\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0648 - mae: 0.0648 - val_loss: 0.0734 - val_mae: 0.0734\n",
       "Epoch 29/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0616 - mae: 0.0616epoch 28: loss: 0.061, val_loss:0.073 val_mae:0.073\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0608 - mae: 0.0608 - val_loss: 0.0734 - val_mae: 0.0734\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0628 - mae: 0.0628 - val_loss: 0.0697 - val_mae: 0.0697\n",
       "Epoch 30/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0627 - mae: 0.0627epoch 29: loss: 0.064, val_loss:0.082 val_mae:0.082\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0643 - mae: 0.0643 - val_loss: 0.0821 - val_mae: 0.0821\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0621 - mae: 0.0621 - val_loss: 0.0714 - val_mae: 0.0714\n",
       "Epoch 31/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0780 - mae: 0.0780epoch 30: loss: 0.076, val_loss:0.068 val_mae:0.068\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0756 - mae: 0.0756 - val_loss: 0.0685 - val_mae: 0.0685\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0615 - mae: 0.0615 - val_loss: 0.0870 - val_mae: 0.0870\n",
       "Epoch 32/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0606 - mae: 0.0606epoch 31: loss: 0.059, val_loss:0.069 val_mae:0.069\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0590 - mae: 0.0590 - val_loss: 0.0691 - val_mae: 0.0691\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0591 - mae: 0.0591 - val_loss: 0.0700 - val_mae: 0.0700\n",
       "Epoch 33/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0580 - mae: 0.0580epoch 32: loss: 0.058, val_loss:0.074 val_mae:0.074\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0581 - mae: 0.0581 - val_loss: 0.0738 - val_mae: 0.0738\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0624 - mae: 0.0624 - val_loss: 0.0698 - val_mae: 0.0698\n",
       "Epoch 34/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0558 - mae: 0.0558epoch 33: loss: 0.059, val_loss:0.069 val_mae:0.069\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0588 - mae: 0.0588 - val_loss: 0.0687 - val_mae: 0.0687\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0573 - mae: 0.0573 - val_loss: 0.1083 - val_mae: 0.1083\n",
       "Epoch 35/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0676 - mae: 0.0676epoch 34: loss: 0.065, val_loss:0.072 val_mae:0.072\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0651 - mae: 0.0651 - val_loss: 0.0721 - val_mae: 0.0721\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0617 - mae: 0.0617 - val_loss: 0.0755 - val_mae: 0.0755\n",
       "Epoch 36/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0532 - mae: 0.0532epoch 35: loss: 0.056, val_loss:0.076 val_mae:0.076\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0555 - mae: 0.0555 - val_loss: 0.0762 - val_mae: 0.0762\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0597 - mae: 0.0597 - val_loss: 0.0745 - val_mae: 0.0745\n",
       "Epoch 37/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0598 - mae: 0.0598epoch 36: loss: 0.060, val_loss:0.067 val_mae:0.067\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0598 - mae: 0.0598 - val_loss: 0.0668 - val_mae: 0.0668\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0597 - mae: 0.0597 - val_loss: 0.0672 - val_mae: 0.0672\n",
       "Epoch 38/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0599 - mae: 0.0599epoch 37: loss: 0.058, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0580 - mae: 0.0580 - val_loss: 0.0614 - val_mae: 0.0614\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0573 - mae: 0.0573 - val_loss: 0.0658 - val_mae: 0.0658\n",
       "Epoch 39/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0571 - mae: 0.0571epoch 38: loss: 0.063, val_loss:0.067 val_mae:0.067\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0626 - mae: 0.0626 - val_loss: 0.0673 - val_mae: 0.0673\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0570 - mae: 0.0570 - val_loss: 0.0639 - val_mae: 0.0639\n",
       "Epoch 40/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0588 - mae: 0.0588epoch 39: loss: 0.058, val_loss:0.082 val_mae:0.082\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0579 - mae: 0.0579 - val_loss: 0.0817 - val_mae: 0.0817\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0602 - mae: 0.0602 - val_loss: 0.0633 - val_mae: 0.0633\n",
       "Epoch 41/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0612 - mae: 0.0612epoch 40: loss: 0.061, val_loss:0.072 val_mae:0.072\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0614 - mae: 0.0614 - val_loss: 0.0722 - val_mae: 0.0722\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0553 - mae: 0.0553 - val_loss: 0.0725 - val_mae: 0.0725\n",
       "Epoch 42/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0605 - mae: 0.0605epoch 41: loss: 0.061, val_loss:0.072 val_mae:0.072\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0607 - mae: 0.0607 - val_loss: 0.0724 - val_mae: 0.0724\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0652 - mae: 0.0652 - val_loss: 0.0935 - val_mae: 0.0935\n",
       "Epoch 43/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0588 - mae: 0.0588epoch 42: loss: 0.059, val_loss:0.080 val_mae:0.080\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0594 - mae: 0.0594 - val_loss: 0.0796 - val_mae: 0.0796\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0562 - mae: 0.0562 - val_loss: 0.0653 - val_mae: 0.0653\n",
       "Epoch 44/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0520 - mae: 0.0520epoch 43: loss: 0.052, val_loss:0.073 val_mae:0.073\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0524 - mae: 0.0524 - val_loss: 0.0734 - val_mae: 0.0734\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0550 - mae: 0.0550 - val_loss: 0.0696 - val_mae: 0.0696\n",
       "Epoch 45/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0559 - mae: 0.0559epoch 44: loss: 0.055, val_loss:0.082 val_mae:0.082\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0551 - mae: 0.0551 - val_loss: 0.0818 - val_mae: 0.0818\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0520 - mae: 0.0520 - val_loss: 0.0677 - val_mae: 0.0677\n",
       "Epoch 46/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0608 - mae: 0.0608epoch 45: loss: 0.058, val_loss:0.068 val_mae:0.068\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0578 - mae: 0.0578 - val_loss: 0.0679 - val_mae: 0.0679\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0558 - mae: 0.0558 - val_loss: 0.0715 - val_mae: 0.0715\n",
       "Epoch 47/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0536 - mae: 0.0536epoch 46: loss: 0.053, val_loss:0.068 val_mae:0.068\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0530 - mae: 0.0530 - val_loss: 0.0685 - val_mae: 0.0685\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0522 - mae: 0.0522 - val_loss: 0.0870 - val_mae: 0.0870\n",
       "Epoch 48/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0559 - mae: 0.0559epoch 47: loss: 0.056, val_loss:0.079 val_mae:0.079\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0562 - mae: 0.0562 - val_loss: 0.0790 - val_mae: 0.0790\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0517 - mae: 0.0517 - val_loss: 0.0819 - val_mae: 0.0819\n",
       "Epoch 49/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0563 - mae: 0.0563epoch 48: loss: 0.057, val_loss:0.079 val_mae:0.079\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0567 - mae: 0.0567 - val_loss: 0.0793 - val_mae: 0.0793\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0573 - mae: 0.0573 - val_loss: 0.0646 - val_mae: 0.0646\n",
       "Epoch 50/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0634 - mae: 0.0634epoch 49: loss: 0.060, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0603 - mae: 0.0603 - val_loss: 0.0631 - val_mae: 0.0631\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0609 - mae: 0.0609 - val_loss: 0.0614 - val_mae: 0.0614\n",
       "Epoch 51/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0510 - mae: 0.0510epoch 50: loss: 0.052, val_loss:0.066 val_mae:0.066\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0517 - mae: 0.0517 - val_loss: 0.0658 - val_mae: 0.0658\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0608 - mae: 0.0608 - val_loss: 0.0658 - val_mae: 0.0658\n",
       "Epoch 52/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0584 - mae: 0.0584epoch 51: loss: 0.056, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0563 - mae: 0.0563 - val_loss: 0.0635 - val_mae: 0.0635\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0555 - mae: 0.0555 - val_loss: 0.0721 - val_mae: 0.0721\n",
       "Epoch 53/300\n",
-      "47/57 [=======================>......] - ETA: 0s - loss: 0.0491 - mae: 0.0491epoch 52: loss: 0.048, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0483 - mae: 0.0483 - val_loss: 0.0631 - val_mae: 0.0631\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0514 - mae: 0.0514 - val_loss: 0.0656 - val_mae: 0.0656\n",
       "Epoch 54/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0462 - mae: 0.0462epoch 53: loss: 0.047, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0469 - mae: 0.0469 - val_loss: 0.0612 - val_mae: 0.0612\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0463 - mae: 0.0463 - val_loss: 0.0682 - val_mae: 0.0682\n",
       "Epoch 55/300\n",
-      "48/57 [========================>.....] - ETA: 0s - loss: 0.0443 - mae: 0.0443epoch 54: loss: 0.045, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0452 - mae: 0.0452 - val_loss: 0.0624 - val_mae: 0.0624\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0633 - mae: 0.0633 - val_loss: 0.0629 - val_mae: 0.0629\n",
       "Epoch 56/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0461 - mae: 0.0461epoch 55: loss: 0.047, val_loss:0.065 val_mae:0.065\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0468 - mae: 0.0468 - val_loss: 0.0651 - val_mae: 0.0651\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0481 - mae: 0.0481 - val_loss: 0.0852 - val_mae: 0.0852\n",
       "Epoch 57/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0539 - mae: 0.0539epoch 56: loss: 0.055, val_loss:0.071 val_mae:0.071\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0548 - mae: 0.0548 - val_loss: 0.0712 - val_mae: 0.0712\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0509 - mae: 0.0509 - val_loss: 0.0650 - val_mae: 0.0650\n",
       "Epoch 58/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0504 - mae: 0.0504epoch 57: loss: 0.049, val_loss:0.069 val_mae:0.069\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0493 - mae: 0.0493 - val_loss: 0.0685 - val_mae: 0.0685\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0582 - mae: 0.0582 - val_loss: 0.0992 - val_mae: 0.0992\n",
       "Epoch 59/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0467 - mae: 0.0467epoch 58: loss: 0.047, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0467 - mae: 0.0467 - val_loss: 0.0605 - val_mae: 0.0605\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0536 - mae: 0.0536 - val_loss: 0.0594 - val_mae: 0.0594\n",
       "Epoch 60/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0485 - mae: 0.0485epoch 59: loss: 0.052, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0522 - mae: 0.0522 - val_loss: 0.0608 - val_mae: 0.0608\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0488 - mae: 0.0488 - val_loss: 0.0651 - val_mae: 0.0651\n",
       "Epoch 61/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0548 - mae: 0.0548epoch 60: loss: 0.059, val_loss:0.071 val_mae:0.071\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0592 - mae: 0.0592 - val_loss: 0.0707 - val_mae: 0.0707\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0551 - mae: 0.0551 - val_loss: 0.0632 - val_mae: 0.0632\n",
       "Epoch 62/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0561 - mae: 0.0561epoch 61: loss: 0.056, val_loss:0.069 val_mae:0.069\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0555 - mae: 0.0555 - val_loss: 0.0690 - val_mae: 0.0690\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0481 - mae: 0.0481 - val_loss: 0.0641 - val_mae: 0.0641\n",
       "Epoch 63/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0452 - mae: 0.0452epoch 62: loss: 0.046, val_loss:0.070 val_mae:0.070\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0458 - mae: 0.0458 - val_loss: 0.0697 - val_mae: 0.0697\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0517 - mae: 0.0517 - val_loss: 0.0851 - val_mae: 0.0851\n",
       "Epoch 64/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0605 - mae: 0.0605epoch 63: loss: 0.058, val_loss:0.065 val_mae:0.065\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0577 - mae: 0.0577 - val_loss: 0.0650 - val_mae: 0.0650\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0496 - mae: 0.0496 - val_loss: 0.0630 - val_mae: 0.0630\n",
       "Epoch 65/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0479 - mae: 0.0479epoch 64: loss: 0.047, val_loss:0.065 val_mae:0.065\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0475 - mae: 0.0475 - val_loss: 0.0653 - val_mae: 0.0653\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0481 - mae: 0.0481 - val_loss: 0.0771 - val_mae: 0.0771\n",
       "Epoch 66/300\n",
-      "47/57 [=======================>......] - ETA: 0s - loss: 0.0491 - mae: 0.0491epoch 65: loss: 0.049, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0486 - mae: 0.0486 - val_loss: 0.0620 - val_mae: 0.0620\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0473 - mae: 0.0473 - val_loss: 0.0675 - val_mae: 0.0675\n",
       "Epoch 67/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0450 - mae: 0.0450epoch 66: loss: 0.046, val_loss:0.072 val_mae:0.072\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0456 - mae: 0.0456 - val_loss: 0.0719 - val_mae: 0.0719\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0476 - mae: 0.0476 - val_loss: 0.0619 - val_mae: 0.0619\n",
       "Epoch 68/300\n",
-      "37/57 [==================>...........] - ETA: 0s - loss: 0.0494 - mae: 0.0494epoch 67: loss: 0.046, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0458 - mae: 0.0458 - val_loss: 0.0617 - val_mae: 0.0617\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0490 - mae: 0.0490 - val_loss: 0.0687 - val_mae: 0.0687\n",
       "Epoch 69/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0470 - mae: 0.0470epoch 68: loss: 0.046, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0460 - mae: 0.0460 - val_loss: 0.0598 - val_mae: 0.0598\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0471 - mae: 0.0471 - val_loss: 0.0746 - val_mae: 0.0746\n",
       "Epoch 70/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0472 - mae: 0.0472epoch 69: loss: 0.047, val_loss:0.065 val_mae:0.065\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0473 - mae: 0.0473 - val_loss: 0.0647 - val_mae: 0.0647\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0477 - mae: 0.0477 - val_loss: 0.0617 - val_mae: 0.0617\n",
       "Epoch 71/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0425 - mae: 0.0425epoch 70: loss: 0.046, val_loss:0.069 val_mae:0.069\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0461 - mae: 0.0461 - val_loss: 0.0693 - val_mae: 0.0693\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0435 - mae: 0.0435 - val_loss: 0.0595 - val_mae: 0.0595\n",
       "Epoch 72/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0465 - mae: 0.0465epoch 71: loss: 0.046, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0463 - mae: 0.0463 - val_loss: 0.0631 - val_mae: 0.0631\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0428 - mae: 0.0428 - val_loss: 0.0637 - val_mae: 0.0637\n",
       "Epoch 73/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0443 - mae: 0.0443epoch 72: loss: 0.045, val_loss:0.071 val_mae:0.071\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0455 - mae: 0.0455 - val_loss: 0.0710 - val_mae: 0.0710\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0447 - mae: 0.0447 - val_loss: 0.0913 - val_mae: 0.0913\n",
       "Epoch 74/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0450 - mae: 0.0450epoch 73: loss: 0.045, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0446 - mae: 0.0446 - val_loss: 0.0645 - val_mae: 0.0645\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0465 - mae: 0.0465 - val_loss: 0.0675 - val_mae: 0.0675\n",
       "Epoch 75/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0545 - mae: 0.0545epoch 74: loss: 0.052, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0521 - mae: 0.0521 - val_loss: 0.0607 - val_mae: 0.0607\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0463 - mae: 0.0463 - val_loss: 0.0657 - val_mae: 0.0657\n",
       "Epoch 76/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0439 - mae: 0.0439epoch 75: loss: 0.044, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0437 - mae: 0.0437 - val_loss: 0.0610 - val_mae: 0.0610\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0451 - mae: 0.0451 - val_loss: 0.0552 - val_mae: 0.0552\n",
       "Epoch 77/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0402 - mae: 0.0402epoch 76: loss: 0.040, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0397 - mae: 0.0397 - val_loss: 0.0617 - val_mae: 0.0617\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0508 - mae: 0.0508 - val_loss: 0.0613 - val_mae: 0.0613\n",
       "Epoch 78/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0440 - mae: 0.0440epoch 77: loss: 0.044, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0443 - mae: 0.0443 - val_loss: 0.0636 - val_mae: 0.0636\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0408 - mae: 0.0408 - val_loss: 0.0587 - val_mae: 0.0587\n",
       "Epoch 79/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0415 - mae: 0.0415epoch 78: loss: 0.044, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0436 - mae: 0.0436 - val_loss: 0.0575 - val_mae: 0.0575\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0468 - mae: 0.0468 - val_loss: 0.0812 - val_mae: 0.0812\n",
       "Epoch 80/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0478 - mae: 0.0478epoch 79: loss: 0.047, val_loss:0.070 val_mae:0.070\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0470 - mae: 0.0470 - val_loss: 0.0704 - val_mae: 0.0704\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0467 - mae: 0.0467 - val_loss: 0.0591 - val_mae: 0.0591\n",
       "Epoch 81/300\n",
-      "47/57 [=======================>......] - ETA: 0s - loss: 0.0417 - mae: 0.0417epoch 80: loss: 0.041, val_loss:0.066 val_mae:0.066\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0412 - mae: 0.0412 - val_loss: 0.0657 - val_mae: 0.0657\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0413 - mae: 0.0413 - val_loss: 0.0593 - val_mae: 0.0593\n",
       "Epoch 82/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0425 - mae: 0.0425epoch 81: loss: 0.045, val_loss:0.068 val_mae:0.068\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0447 - mae: 0.0447 - val_loss: 0.0678 - val_mae: 0.0678\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0526 - mae: 0.0526 - val_loss: 0.0672 - val_mae: 0.0672\n",
       "Epoch 83/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0476 - mae: 0.0476epoch 82: loss: 0.046, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0456 - mae: 0.0456 - val_loss: 0.0579 - val_mae: 0.0579\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0457 - mae: 0.0457 - val_loss: 0.0659 - val_mae: 0.0659\n",
       "Epoch 84/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0449 - mae: 0.0449epoch 83: loss: 0.045, val_loss:0.067 val_mae:0.067\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0450 - mae: 0.0450 - val_loss: 0.0666 - val_mae: 0.0666\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0472 - mae: 0.0472 - val_loss: 0.0595 - val_mae: 0.0595\n",
       "Epoch 85/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0445 - mae: 0.0445epoch 84: loss: 0.045, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0452 - mae: 0.0452 - val_loss: 0.0601 - val_mae: 0.0601\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0483 - mae: 0.0483 - val_loss: 0.0657 - val_mae: 0.0657\n",
       "Epoch 86/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0447 - mae: 0.0447epoch 85: loss: 0.043, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0433 - mae: 0.0433 - val_loss: 0.0581 - val_mae: 0.0581\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0440 - mae: 0.0440 - val_loss: 0.0616 - val_mae: 0.0616\n",
       "Epoch 87/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0409 - mae: 0.0409epoch 86: loss: 0.042, val_loss:0.067 val_mae:0.067\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0416 - mae: 0.0416 - val_loss: 0.0667 - val_mae: 0.0667\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0413 - mae: 0.0413 - val_loss: 0.0692 - val_mae: 0.0692\n",
       "Epoch 88/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0437 - mae: 0.0437epoch 87: loss: 0.042, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0424 - mae: 0.0424 - val_loss: 0.0582 - val_mae: 0.0582\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0408 - mae: 0.0408 - val_loss: 0.0639 - val_mae: 0.0639\n",
       "Epoch 89/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0390 - mae: 0.0390epoch 88: loss: 0.039, val_loss:0.066 val_mae:0.066\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0394 - mae: 0.0394 - val_loss: 0.0663 - val_mae: 0.0663\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0449 - mae: 0.0449 - val_loss: 0.0600 - val_mae: 0.0600\n",
       "Epoch 90/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0520 - mae: 0.0520epoch 89: loss: 0.050, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0501 - mae: 0.0501 - val_loss: 0.0645 - val_mae: 0.0645\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0365 - mae: 0.0365 - val_loss: 0.0677 - val_mae: 0.0677\n",
       "Epoch 91/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0440 - mae: 0.0440epoch 90: loss: 0.044, val_loss:0.079 val_mae:0.079\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0439 - mae: 0.0439 - val_loss: 0.0790 - val_mae: 0.0790\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0413 - mae: 0.0413 - val_loss: 0.0550 - val_mae: 0.0550\n",
       "Epoch 92/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0467 - mae: 0.0467epoch 91: loss: 0.045, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0449 - mae: 0.0449 - val_loss: 0.0595 - val_mae: 0.0595\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0390 - mae: 0.0390 - val_loss: 0.0570 - val_mae: 0.0570\n",
       "Epoch 93/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0394 - mae: 0.0394epoch 92: loss: 0.039, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0391 - mae: 0.0391 - val_loss: 0.0604 - val_mae: 0.0604\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0410 - mae: 0.0410 - val_loss: 0.0700 - val_mae: 0.0700\n",
       "Epoch 94/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0409 - mae: 0.0409epoch 93: loss: 0.043, val_loss:0.078 val_mae:0.078\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0430 - mae: 0.0430 - val_loss: 0.0783 - val_mae: 0.0783\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0451 - mae: 0.0451 - val_loss: 0.0598 - val_mae: 0.0598\n",
       "Epoch 95/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0469 - mae: 0.0469epoch 94: loss: 0.045, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0455 - mae: 0.0455 - val_loss: 0.0601 - val_mae: 0.0601\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0528 - mae: 0.0528 - val_loss: 0.0693 - val_mae: 0.0693\n",
       "Epoch 96/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0440 - mae: 0.0440epoch 95: loss: 0.043, val_loss:0.073 val_mae:0.073\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0426 - mae: 0.0426 - val_loss: 0.0732 - val_mae: 0.0732\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0426 - mae: 0.0426 - val_loss: 0.0580 - val_mae: 0.0580\n",
       "Epoch 97/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0391 - mae: 0.0391epoch 96: loss: 0.041, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0408 - mae: 0.0408 - val_loss: 0.0574 - val_mae: 0.0574\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0378 - mae: 0.0378 - val_loss: 0.0591 - val_mae: 0.0591\n",
       "Epoch 98/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0391 - mae: 0.0391epoch 97: loss: 0.039, val_loss:0.071 val_mae:0.071\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0392 - mae: 0.0392 - val_loss: 0.0712 - val_mae: 0.0712\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0441 - mae: 0.0441 - val_loss: 0.0567 - val_mae: 0.0567\n",
       "Epoch 99/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0401 - mae: 0.0401epoch 98: loss: 0.039, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0392 - mae: 0.0392 - val_loss: 0.0610 - val_mae: 0.0610\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0381 - mae: 0.0381 - val_loss: 0.0581 - val_mae: 0.0581\n",
       "Epoch 100/300\n",
-      "48/57 [========================>.....] - ETA: 0s - loss: 0.0362 - mae: 0.0362epoch 99: loss: 0.036, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0362 - mae: 0.0362 - val_loss: 0.0623 - val_mae: 0.0623\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0477 - mae: 0.0477 - val_loss: 0.0579 - val_mae: 0.0579\n",
       "Epoch 101/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0339 - mae: 0.0339epoch 100: loss: 0.035, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0350 - mae: 0.0350 - val_loss: 0.0611 - val_mae: 0.0611\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0351 - mae: 0.0351 - val_loss: 0.0566 - val_mae: 0.0566\n",
       "Epoch 102/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0347 - mae: 0.0347epoch 101: loss: 0.035, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0352 - mae: 0.0352 - val_loss: 0.0627 - val_mae: 0.0627\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0399 - mae: 0.0399 - val_loss: 0.0616 - val_mae: 0.0616\n",
       "Epoch 103/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0387 - mae: 0.0387epoch 102: loss: 0.039, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0389 - mae: 0.0389 - val_loss: 0.0587 - val_mae: 0.0587\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0409 - mae: 0.0409 - val_loss: 0.0680 - val_mae: 0.0680\n",
       "Epoch 104/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0375 - mae: 0.0375epoch 103: loss: 0.039, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0392 - mae: 0.0392 - val_loss: 0.0624 - val_mae: 0.0624\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0412 - mae: 0.0412 - val_loss: 0.0648 - val_mae: 0.0648\n",
       "Epoch 105/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0367 - mae: 0.0367epoch 104: loss: 0.038, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0381 - mae: 0.0381 - val_loss: 0.0573 - val_mae: 0.0573\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0398 - mae: 0.0398 - val_loss: 0.0566 - val_mae: 0.0566\n",
       "Epoch 106/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0504 - mae: 0.0504epoch 105: loss: 0.047, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0470 - mae: 0.0470 - val_loss: 0.0600 - val_mae: 0.0600\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0369 - mae: 0.0369 - val_loss: 0.0614 - val_mae: 0.0614\n",
       "Epoch 107/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0389 - mae: 0.0389epoch 106: loss: 0.038, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0383 - mae: 0.0383 - val_loss: 0.0550 - val_mae: 0.0550\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0355 - mae: 0.0355 - val_loss: 0.0575 - val_mae: 0.0575\n",
       "Epoch 108/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0335 - mae: 0.0335epoch 107: loss: 0.034, val_loss:0.070 val_mae:0.070\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0342 - mae: 0.0342 - val_loss: 0.0697 - val_mae: 0.0697\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0367 - mae: 0.0367 - val_loss: 0.0616 - val_mae: 0.0616\n",
       "Epoch 109/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0354 - mae: 0.0354epoch 108: loss: 0.036, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0362 - mae: 0.0362 - val_loss: 0.0558 - val_mae: 0.0558\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0393 - mae: 0.0393 - val_loss: 0.0646 - val_mae: 0.0646\n",
       "Epoch 110/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0371 - mae: 0.0371epoch 109: loss: 0.037, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0369 - mae: 0.0369 - val_loss: 0.0572 - val_mae: 0.0572\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0403 - mae: 0.0403 - val_loss: 0.0603 - val_mae: 0.0603\n",
       "Epoch 111/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0366 - mae: 0.0366epoch 110: loss: 0.036, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0363 - mae: 0.0363 - val_loss: 0.0629 - val_mae: 0.0629\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0371 - mae: 0.0371 - val_loss: 0.0564 - val_mae: 0.0564\n",
       "Epoch 112/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0403 - mae: 0.0403epoch 111: loss: 0.039, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0390 - mae: 0.0390 - val_loss: 0.0579 - val_mae: 0.0579\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0361 - mae: 0.0361 - val_loss: 0.0572 - val_mae: 0.0572\n",
       "Epoch 113/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0382 - mae: 0.0382epoch 112: loss: 0.037, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0368 - mae: 0.0368 - val_loss: 0.0566 - val_mae: 0.0566\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0367 - mae: 0.0367 - val_loss: 0.0552 - val_mae: 0.0552\n",
       "Epoch 114/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0372 - mae: 0.0372epoch 113: loss: 0.038, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0377 - mae: 0.0377 - val_loss: 0.0601 - val_mae: 0.0601\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0412 - mae: 0.0412 - val_loss: 0.0575 - val_mae: 0.0575\n",
       "Epoch 115/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0330 - mae: 0.0330epoch 114: loss: 0.034, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0342 - mae: 0.0342 - val_loss: 0.0551 - val_mae: 0.0551\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0366 - mae: 0.0366 - val_loss: 0.0621 - val_mae: 0.0621\n",
       "Epoch 116/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0362 - mae: 0.0362epoch 115: loss: 0.036, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0360 - mae: 0.0360 - val_loss: 0.0606 - val_mae: 0.0606\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0365 - mae: 0.0365 - val_loss: 0.0571 - val_mae: 0.0571\n",
       "Epoch 117/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0358 - mae: 0.0358epoch 116: loss: 0.038, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0376 - mae: 0.0376 - val_loss: 0.0639 - val_mae: 0.0639\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0402 - mae: 0.0402 - val_loss: 0.0532 - val_mae: 0.0532\n",
       "Epoch 118/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0386 - mae: 0.0386epoch 117: loss: 0.039, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0391 - mae: 0.0391 - val_loss: 0.0629 - val_mae: 0.0629\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0376 - mae: 0.0376 - val_loss: 0.0575 - val_mae: 0.0575\n",
       "Epoch 119/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0421 - mae: 0.0421epoch 118: loss: 0.042, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0416 - mae: 0.0416 - val_loss: 0.0585 - val_mae: 0.0585\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0347 - mae: 0.0347 - val_loss: 0.0536 - val_mae: 0.0536\n",
       "Epoch 120/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0388 - mae: 0.0388epoch 119: loss: 0.038, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0378 - mae: 0.0378 - val_loss: 0.0591 - val_mae: 0.0591\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0373 - mae: 0.0373 - val_loss: 0.0730 - val_mae: 0.0730\n",
       "Epoch 121/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0351 - mae: 0.0351epoch 120: loss: 0.035, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0348 - mae: 0.0348 - val_loss: 0.0612 - val_mae: 0.0612\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0384 - mae: 0.0384 - val_loss: 0.0571 - val_mae: 0.0571\n",
       "Epoch 122/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0465 - mae: 0.0465epoch 121: loss: 0.045, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0449 - mae: 0.0449 - val_loss: 0.0588 - val_mae: 0.0588\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0423 - mae: 0.0423 - val_loss: 0.0653 - val_mae: 0.0653\n",
       "Epoch 123/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0346 - mae: 0.0346epoch 122: loss: 0.034, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0340 - mae: 0.0340 - val_loss: 0.0557 - val_mae: 0.0557\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0385 - mae: 0.0385 - val_loss: 0.0548 - val_mae: 0.0548\n",
       "Epoch 124/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0331 - mae: 0.0331epoch 123: loss: 0.034, val_loss:0.073 val_mae:0.073\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0336 - mae: 0.0336 - val_loss: 0.0729 - val_mae: 0.0729\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0387 - mae: 0.0387 - val_loss: 0.0607 - val_mae: 0.0607\n",
       "Epoch 125/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0324 - mae: 0.0324epoch 124: loss: 0.034, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0337 - mae: 0.0337 - val_loss: 0.0588 - val_mae: 0.0588\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0380 - mae: 0.0380 - val_loss: 0.0568 - val_mae: 0.0568\n",
       "Epoch 126/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0319 - mae: 0.0319epoch 125: loss: 0.035, val_loss:0.070 val_mae:0.070\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0346 - mae: 0.0346 - val_loss: 0.0699 - val_mae: 0.0699\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0333 - mae: 0.0333 - val_loss: 0.0611 - val_mae: 0.0611\n",
       "Epoch 127/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0357 - mae: 0.0357epoch 126: loss: 0.037, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0369 - mae: 0.0369 - val_loss: 0.0618 - val_mae: 0.0618\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0382 - mae: 0.0382 - val_loss: 0.0576 - val_mae: 0.0576\n",
       "Epoch 128/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0323 - mae: 0.0323epoch 127: loss: 0.032, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0318 - mae: 0.0318 - val_loss: 0.0595 - val_mae: 0.0595\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0347 - mae: 0.0347 - val_loss: 0.0548 - val_mae: 0.0548\n",
       "Epoch 129/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0429 - mae: 0.0429epoch 128: loss: 0.042, val_loss:0.065 val_mae:0.065\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0424 - mae: 0.0424 - val_loss: 0.0648 - val_mae: 0.0648\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0320 - mae: 0.0320 - val_loss: 0.0576 - val_mae: 0.0576\n",
       "Epoch 130/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0385 - mae: 0.0385epoch 129: loss: 0.039, val_loss:0.067 val_mae:0.067\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0394 - mae: 0.0394 - val_loss: 0.0670 - val_mae: 0.0670\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0310 - mae: 0.0310 - val_loss: 0.0623 - val_mae: 0.0623\n",
       "Epoch 131/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0357 - mae: 0.0357epoch 130: loss: 0.035, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0352 - mae: 0.0352 - val_loss: 0.0559 - val_mae: 0.0559\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0436 - mae: 0.0436 - val_loss: 0.0583 - val_mae: 0.0583\n",
       "Epoch 132/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0335 - mae: 0.0335epoch 131: loss: 0.034, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0340 - mae: 0.0340 - val_loss: 0.0628 - val_mae: 0.0628\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0370 - mae: 0.0370 - val_loss: 0.0615 - val_mae: 0.0615\n",
       "Epoch 133/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0321 - mae: 0.0321epoch 132: loss: 0.033, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0329 - mae: 0.0329 - val_loss: 0.0592 - val_mae: 0.0592\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0393 - mae: 0.0393 - val_loss: 0.0607 - val_mae: 0.0607\n",
       "Epoch 134/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0344 - mae: 0.0344epoch 133: loss: 0.034, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0336 - mae: 0.0336 - val_loss: 0.0601 - val_mae: 0.0601\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0380 - mae: 0.0380 - val_loss: 0.0537 - val_mae: 0.0537\n",
       "Epoch 135/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0342 - mae: 0.0342epoch 134: loss: 0.034, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0341 - mae: 0.0341 - val_loss: 0.0621 - val_mae: 0.0621\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0377 - mae: 0.0377 - val_loss: 0.0592 - val_mae: 0.0592\n",
       "Epoch 136/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0345 - mae: 0.0345epoch 135: loss: 0.034, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0340 - mae: 0.0340 - val_loss: 0.0578 - val_mae: 0.0578\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0308 - mae: 0.0308 - val_loss: 0.0623 - val_mae: 0.0623\n",
       "Epoch 137/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0336 - mae: 0.0336epoch 136: loss: 0.035, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0354 - mae: 0.0354 - val_loss: 0.0589 - val_mae: 0.0589\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0368 - mae: 0.0368 - val_loss: 0.0559 - val_mae: 0.0559\n",
       "Epoch 138/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0395 - mae: 0.0395epoch 137: loss: 0.038, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0383 - mae: 0.0383 - val_loss: 0.0641 - val_mae: 0.0641\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0372 - mae: 0.0372 - val_loss: 0.0525 - val_mae: 0.0525\n",
       "Epoch 139/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0399 - mae: 0.0399epoch 138: loss: 0.038, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0382 - mae: 0.0382 - val_loss: 0.0548 - val_mae: 0.0548\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0335 - mae: 0.0335 - val_loss: 0.0528 - val_mae: 0.0528\n",
       "Epoch 140/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0308 - mae: 0.0308epoch 139: loss: 0.032, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0322 - mae: 0.0322 - val_loss: 0.0574 - val_mae: 0.0574\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0319 - mae: 0.0319 - val_loss: 0.0556 - val_mae: 0.0556\n",
       "Epoch 141/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0432 - mae: 0.0432epoch 140: loss: 0.044, val_loss:0.067 val_mae:0.067\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0440 - mae: 0.0440 - val_loss: 0.0665 - val_mae: 0.0665\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0331 - mae: 0.0331 - val_loss: 0.0542 - val_mae: 0.0542\n",
       "Epoch 142/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0409 - mae: 0.0409epoch 141: loss: 0.039, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0390 - mae: 0.0390 - val_loss: 0.0564 - val_mae: 0.0564\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0317 - mae: 0.0317 - val_loss: 0.0541 - val_mae: 0.0541\n",
       "Epoch 143/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0314 - mae: 0.0314epoch 142: loss: 0.030, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0303 - mae: 0.0303 - val_loss: 0.0630 - val_mae: 0.0630\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0294 - mae: 0.0294 - val_loss: 0.0643 - val_mae: 0.0643\n",
       "Epoch 144/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0322 - mae: 0.0322epoch 143: loss: 0.031, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0314 - mae: 0.0314 - val_loss: 0.0548 - val_mae: 0.0548\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0383 - mae: 0.0383 - val_loss: 0.0594 - val_mae: 0.0594\n",
       "Epoch 145/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0306 - mae: 0.0306epoch 144: loss: 0.030, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0299 - mae: 0.0299 - val_loss: 0.0525 - val_mae: 0.0525\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0364 - mae: 0.0364 - val_loss: 0.0759 - val_mae: 0.0759\n",
       "Epoch 146/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0297 - mae: 0.0297epoch 145: loss: 0.031, val_loss:0.066 val_mae:0.066\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0313 - mae: 0.0313 - val_loss: 0.0659 - val_mae: 0.0659\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0412 - mae: 0.0412 - val_loss: 0.0561 - val_mae: 0.0561\n",
       "Epoch 147/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0373 - mae: 0.0373epoch 146: loss: 0.036, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0358 - mae: 0.0358 - val_loss: 0.0586 - val_mae: 0.0586\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0316 - mae: 0.0316 - val_loss: 0.0542 - val_mae: 0.0542\n",
       "Epoch 148/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0352 - mae: 0.0352epoch 147: loss: 0.034, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0336 - mae: 0.0336 - val_loss: 0.0576 - val_mae: 0.0576\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0318 - mae: 0.0318 - val_loss: 0.0659 - val_mae: 0.0659\n",
       "Epoch 149/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0332 - mae: 0.0332epoch 148: loss: 0.034, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0337 - mae: 0.0337 - val_loss: 0.0590 - val_mae: 0.0590\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0335 - mae: 0.0335 - val_loss: 0.0535 - val_mae: 0.0535\n",
       "Epoch 150/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0364 - mae: 0.0364epoch 149: loss: 0.034, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0343 - mae: 0.0343 - val_loss: 0.0578 - val_mae: 0.0578\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0306 - mae: 0.0306 - val_loss: 0.0542 - val_mae: 0.0542\n",
       "Epoch 151/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0310 - mae: 0.0310epoch 150: loss: 0.030, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0305 - mae: 0.0305 - val_loss: 0.0611 - val_mae: 0.0611\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0308 - mae: 0.0308 - val_loss: 0.0564 - val_mae: 0.0564\n",
       "Epoch 152/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0319 - mae: 0.0319epoch 151: loss: 0.031, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0310 - mae: 0.0310 - val_loss: 0.0571 - val_mae: 0.0571\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0317 - mae: 0.0317 - val_loss: 0.0592 - val_mae: 0.0592\n",
       "Epoch 153/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0300 - mae: 0.0300epoch 152: loss: 0.030, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0300 - mae: 0.0300 - val_loss: 0.0560 - val_mae: 0.0560\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0315 - mae: 0.0315 - val_loss: 0.0538 - val_mae: 0.0538\n",
       "Epoch 154/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0301 - mae: 0.0301epoch 153: loss: 0.030, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0296 - mae: 0.0296 - val_loss: 0.0544 - val_mae: 0.0544\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0324 - mae: 0.0324 - val_loss: 0.0643 - val_mae: 0.0643\n",
       "Epoch 155/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0285 - mae: 0.0285epoch 154: loss: 0.031, val_loss:0.076 val_mae:0.076\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0305 - mae: 0.0305 - val_loss: 0.0759 - val_mae: 0.0759\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0402 - mae: 0.0402 - val_loss: 0.0575 - val_mae: 0.0575\n",
       "Epoch 156/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0346 - mae: 0.0346epoch 155: loss: 0.033, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0332 - mae: 0.0332 - val_loss: 0.0608 - val_mae: 0.0608\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0340 - mae: 0.0340 - val_loss: 0.0569 - val_mae: 0.0569\n",
       "Epoch 157/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0341 - mae: 0.0341epoch 156: loss: 0.034, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0342 - mae: 0.0342 - val_loss: 0.0585 - val_mae: 0.0585\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0300 - mae: 0.0300 - val_loss: 0.0559 - val_mae: 0.0559\n",
       "Epoch 158/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0297 - mae: 0.0297epoch 157: loss: 0.029, val_loss:0.066 val_mae:0.066\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0293 - mae: 0.0293 - val_loss: 0.0656 - val_mae: 0.0656\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0324 - mae: 0.0324 - val_loss: 0.0565 - val_mae: 0.0565\n",
       "Epoch 159/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0315 - mae: 0.0315epoch 158: loss: 0.031, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0306 - mae: 0.0306 - val_loss: 0.0572 - val_mae: 0.0572\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0296 - mae: 0.0296 - val_loss: 0.0547 - val_mae: 0.0547\n",
       "Epoch 160/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0301 - mae: 0.0301epoch 159: loss: 0.032, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0320 - mae: 0.0320 - val_loss: 0.0629 - val_mae: 0.0629\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0314 - mae: 0.0314 - val_loss: 0.0538 - val_mae: 0.0538\n",
       "Epoch 161/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.0405 - mae: 0.0405epoch 160: loss: 0.039, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0392 - mae: 0.0392 - val_loss: 0.0637 - val_mae: 0.0637\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0305 - mae: 0.0305 - val_loss: 0.0583 - val_mae: 0.0583\n",
       "Epoch 162/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0294 - mae: 0.0294epoch 161: loss: 0.030, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0296 - mae: 0.0296 - val_loss: 0.0559 - val_mae: 0.0559\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0317 - mae: 0.0317 - val_loss: 0.0569 - val_mae: 0.0569\n",
       "Epoch 163/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0293 - mae: 0.0293epoch 162: loss: 0.029, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0291 - mae: 0.0291 - val_loss: 0.0529 - val_mae: 0.0529\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0290 - mae: 0.0290 - val_loss: 0.0510 - val_mae: 0.0510\n",
       "Epoch 164/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0283 - mae: 0.0283epoch 163: loss: 0.030, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0297 - mae: 0.0297 - val_loss: 0.0602 - val_mae: 0.0602\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0283 - mae: 0.0283 - val_loss: 0.0550 - val_mae: 0.0550\n",
       "Epoch 165/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0311 - mae: 0.0311epoch 164: loss: 0.032, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0316 - mae: 0.0316 - val_loss: 0.0584 - val_mae: 0.0584\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0298 - mae: 0.0298 - val_loss: 0.0516 - val_mae: 0.0516\n",
       "Epoch 166/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0332 - mae: 0.0332epoch 165: loss: 0.033, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0331 - mae: 0.0331 - val_loss: 0.0543 - val_mae: 0.0543\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0323 - mae: 0.0323 - val_loss: 0.0539 - val_mae: 0.0539\n",
       "Epoch 167/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0348 - mae: 0.0348epoch 166: loss: 0.034, val_loss:0.063 val_mae:0.063\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0344 - mae: 0.0344 - val_loss: 0.0632 - val_mae: 0.0632\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0323 - mae: 0.0323 - val_loss: 0.0504 - val_mae: 0.0504\n",
       "Epoch 168/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0300 - mae: 0.0300epoch 167: loss: 0.030, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0295 - mae: 0.0295 - val_loss: 0.0593 - val_mae: 0.0593\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0308 - mae: 0.0308 - val_loss: 0.0596 - val_mae: 0.0596\n",
       "Epoch 169/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0288 - mae: 0.0288epoch 168: loss: 0.030, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0302 - mae: 0.0302 - val_loss: 0.0605 - val_mae: 0.0605\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0294 - mae: 0.0294 - val_loss: 0.0519 - val_mae: 0.0519\n",
       "Epoch 170/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0303 - mae: 0.0303epoch 169: loss: 0.031, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0305 - mae: 0.0305 - val_loss: 0.0637 - val_mae: 0.0637\n",
+      "57/57 [==============================] - 0s 2ms/step - loss: 0.0302 - mae: 0.0302 - val_loss: 0.0512 - val_mae: 0.0512\n",
       "Epoch 171/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0307 - mae: 0.0307epoch 170: loss: 0.031, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0314 - mae: 0.0314 - val_loss: 0.0577 - val_mae: 0.0577\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0361 - mae: 0.0361 - val_loss: 0.0704 - val_mae: 0.0704\n",
       "Epoch 172/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0348 - mae: 0.0348epoch 171: loss: 0.037, val_loss:0.077 val_mae:0.077\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0366 - mae: 0.0366 - val_loss: 0.0773 - val_mae: 0.0773\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0366 - mae: 0.0366 - val_loss: 0.0733 - val_mae: 0.0733\n",
       "Epoch 173/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0360 - mae: 0.0360epoch 172: loss: 0.034, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0340 - mae: 0.0340 - val_loss: 0.0561 - val_mae: 0.0561\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0319 - mae: 0.0319 - val_loss: 0.0612 - val_mae: 0.0612\n",
       "Epoch 174/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0292 - mae: 0.0292epoch 173: loss: 0.028, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0284 - mae: 0.0284 - val_loss: 0.0589 - val_mae: 0.0589\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0335 - mae: 0.0335 - val_loss: 0.0577 - val_mae: 0.0577\n",
       "Epoch 175/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0276 - mae: 0.0276epoch 174: loss: 0.028, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0281 - mae: 0.0281 - val_loss: 0.0567 - val_mae: 0.0567\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0312 - mae: 0.0312 - val_loss: 0.0567 - val_mae: 0.0567\n",
       "Epoch 176/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0297 - mae: 0.0297epoch 175: loss: 0.030, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0297 - mae: 0.0297 - val_loss: 0.0562 - val_mae: 0.0562\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0281 - mae: 0.0281 - val_loss: 0.0534 - val_mae: 0.0534\n",
       "Epoch 177/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0292 - mae: 0.0292epoch 176: loss: 0.030, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0295 - mae: 0.0295 - val_loss: 0.0614 - val_mae: 0.0614\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0364 - mae: 0.0364 - val_loss: 0.0755 - val_mae: 0.0755\n",
       "Epoch 178/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0299 - mae: 0.0299epoch 177: loss: 0.030, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0303 - mae: 0.0303 - val_loss: 0.0549 - val_mae: 0.0549\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0350 - mae: 0.0350 - val_loss: 0.0538 - val_mae: 0.0538\n",
       "Epoch 179/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0279 - mae: 0.0279epoch 178: loss: 0.028, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0284 - mae: 0.0284 - val_loss: 0.0582 - val_mae: 0.0582\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0300 - mae: 0.0300 - val_loss: 0.0504 - val_mae: 0.0504\n",
       "Epoch 180/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0289 - mae: 0.0289epoch 179: loss: 0.029, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0285 - mae: 0.0285 - val_loss: 0.0528 - val_mae: 0.0528\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0266 - mae: 0.0266 - val_loss: 0.0568 - val_mae: 0.0568\n",
       "Epoch 181/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0286 - mae: 0.0286epoch 180: loss: 0.029, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0287 - mae: 0.0287 - val_loss: 0.0599 - val_mae: 0.0599\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0329 - mae: 0.0329 - val_loss: 0.0585 - val_mae: 0.0585\n",
       "Epoch 182/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0341 - mae: 0.0341epoch 181: loss: 0.033, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0331 - mae: 0.0331 - val_loss: 0.0554 - val_mae: 0.0554\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0293 - mae: 0.0293 - val_loss: 0.0550 - val_mae: 0.0550\n",
       "Epoch 183/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0277 - mae: 0.0277epoch 182: loss: 0.028, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0275 - mae: 0.0275 - val_loss: 0.0600 - val_mae: 0.0600\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0303 - mae: 0.0303 - val_loss: 0.0559 - val_mae: 0.0559\n",
       "Epoch 184/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0256 - mae: 0.0256epoch 183: loss: 0.025, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0255 - mae: 0.0255 - val_loss: 0.0556 - val_mae: 0.0556\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0316 - mae: 0.0316 - val_loss: 0.0554 - val_mae: 0.0554\n",
       "Epoch 185/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0268 - mae: 0.0268epoch 184: loss: 0.027, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0267 - mae: 0.0267 - val_loss: 0.0534 - val_mae: 0.0534\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0290 - mae: 0.0290 - val_loss: 0.0514 - val_mae: 0.0514\n",
       "Epoch 186/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0266 - mae: 0.0266epoch 185: loss: 0.026, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0261 - mae: 0.0261 - val_loss: 0.0563 - val_mae: 0.0563\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0299 - mae: 0.0299 - val_loss: 0.0577 - val_mae: 0.0577\n",
       "Epoch 187/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0275 - mae: 0.0275epoch 186: loss: 0.029, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0287 - mae: 0.0287 - val_loss: 0.0572 - val_mae: 0.0572\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0274 - mae: 0.0274 - val_loss: 0.0506 - val_mae: 0.0506\n",
       "Epoch 188/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0342 - mae: 0.0342epoch 187: loss: 0.032, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0324 - mae: 0.0324 - val_loss: 0.0553 - val_mae: 0.0553\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0272 - mae: 0.0272 - val_loss: 0.0514 - val_mae: 0.0514\n",
       "Epoch 189/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0291 - mae: 0.0291epoch 188: loss: 0.030, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0300 - mae: 0.0300 - val_loss: 0.0564 - val_mae: 0.0564\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0267 - mae: 0.0267 - val_loss: 0.0556 - val_mae: 0.0556\n",
       "Epoch 190/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0273 - mae: 0.0273epoch 189: loss: 0.028, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0281 - mae: 0.0281 - val_loss: 0.0551 - val_mae: 0.0551\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0278 - mae: 0.0278 - val_loss: 0.0576 - val_mae: 0.0576\n",
       "Epoch 191/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0268 - mae: 0.0268epoch 190: loss: 0.027, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0275 - mae: 0.0275 - val_loss: 0.0636 - val_mae: 0.0636\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0279 - mae: 0.0279 - val_loss: 0.0539 - val_mae: 0.0539\n",
       "Epoch 192/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0286 - mae: 0.0286epoch 191: loss: 0.028, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0284 - mae: 0.0284 - val_loss: 0.0527 - val_mae: 0.0527\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0305 - mae: 0.0305 - val_loss: 0.0585 - val_mae: 0.0585\n",
       "Epoch 193/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.0323 - mae: 0.0323epoch 192: loss: 0.031, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0315 - mae: 0.0315 - val_loss: 0.0576 - val_mae: 0.0576\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0289 - mae: 0.0289 - val_loss: 0.0543 - val_mae: 0.0543\n",
       "Epoch 194/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0291 - mae: 0.0291epoch 193: loss: 0.029, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0295 - mae: 0.0295 - val_loss: 0.0564 - val_mae: 0.0564\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0285 - mae: 0.0285 - val_loss: 0.0510 - val_mae: 0.0510\n",
       "Epoch 195/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0276 - mae: 0.0276epoch 194: loss: 0.028, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0280 - mae: 0.0280 - val_loss: 0.0583 - val_mae: 0.0583\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0307 - mae: 0.0307 - val_loss: 0.0500 - val_mae: 0.0500\n",
       "Epoch 196/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0279 - mae: 0.0279epoch 195: loss: 0.028, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0277 - mae: 0.0277 - val_loss: 0.0591 - val_mae: 0.0591\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0334 - mae: 0.0334 - val_loss: 0.0521 - val_mae: 0.0521\n",
       "Epoch 197/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0256 - mae: 0.0256epoch 196: loss: 0.025, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0247 - mae: 0.0247 - val_loss: 0.0552 - val_mae: 0.0552\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0306 - mae: 0.0306 - val_loss: 0.0594 - val_mae: 0.0594\n",
       "Epoch 198/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0289 - mae: 0.0289epoch 197: loss: 0.029, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0290 - mae: 0.0290 - val_loss: 0.0534 - val_mae: 0.0534\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0287 - mae: 0.0287 - val_loss: 0.0497 - val_mae: 0.0497\n",
       "Epoch 199/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0285 - mae: 0.0285epoch 198: loss: 0.029, val_loss:0.066 val_mae:0.066\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0293 - mae: 0.0293 - val_loss: 0.0655 - val_mae: 0.0655\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0302 - mae: 0.0302 - val_loss: 0.0652 - val_mae: 0.0652\n",
       "Epoch 200/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0286 - mae: 0.0286epoch 199: loss: 0.028, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0283 - mae: 0.0283 - val_loss: 0.0605 - val_mae: 0.0605\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0423 - mae: 0.0423 - val_loss: 0.0547 - val_mae: 0.0547\n",
       "Epoch 201/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0270 - mae: 0.0270epoch 200: loss: 0.026, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0261 - mae: 0.0261 - val_loss: 0.0593 - val_mae: 0.0593\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0270 - mae: 0.0270 - val_loss: 0.0495 - val_mae: 0.0495\n",
       "Epoch 202/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0294 - mae: 0.0294epoch 201: loss: 0.029, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0293 - mae: 0.0293 - val_loss: 0.0550 - val_mae: 0.0550\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0259 - mae: 0.0259 - val_loss: 0.0559 - val_mae: 0.0559\n",
       "Epoch 203/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0324 - mae: 0.0324epoch 202: loss: 0.032, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0322 - mae: 0.0322 - val_loss: 0.0550 - val_mae: 0.0550\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0298 - mae: 0.0298 - val_loss: 0.0544 - val_mae: 0.0544\n",
       "Epoch 204/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0301 - mae: 0.0301epoch 203: loss: 0.030, val_loss:0.072 val_mae:0.072\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0301 - mae: 0.0301 - val_loss: 0.0717 - val_mae: 0.0717\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0255 - mae: 0.0255 - val_loss: 0.0508 - val_mae: 0.0508\n",
       "Epoch 205/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0348 - mae: 0.0348epoch 204: loss: 0.033, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0335 - mae: 0.0335 - val_loss: 0.0641 - val_mae: 0.0641\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0240 - mae: 0.0240 - val_loss: 0.0513 - val_mae: 0.0513\n",
       "Epoch 206/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0298 - mae: 0.0298epoch 205: loss: 0.029, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0293 - mae: 0.0293 - val_loss: 0.0547 - val_mae: 0.0547\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0251 - mae: 0.0251 - val_loss: 0.0504 - val_mae: 0.0504\n",
       "Epoch 207/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0247 - mae: 0.0247epoch 206: loss: 0.026, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0255 - mae: 0.0255 - val_loss: 0.0536 - val_mae: 0.0536\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0264 - mae: 0.0264 - val_loss: 0.0524 - val_mae: 0.0524\n",
       "Epoch 208/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.0289 - mae: 0.0289epoch 207: loss: 0.028, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0284 - mae: 0.0284 - val_loss: 0.0537 - val_mae: 0.0537\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0247 - mae: 0.0247 - val_loss: 0.0562 - val_mae: 0.0562\n",
       "Epoch 209/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0271 - mae: 0.0271epoch 208: loss: 0.026, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0259 - mae: 0.0259 - val_loss: 0.0577 - val_mae: 0.0577\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0267 - mae: 0.0267 - val_loss: 0.0506 - val_mae: 0.0506\n",
       "Epoch 210/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0264 - mae: 0.0264epoch 209: loss: 0.026, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0261 - mae: 0.0261 - val_loss: 0.0591 - val_mae: 0.0591\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0253 - mae: 0.0253 - val_loss: 0.0523 - val_mae: 0.0523\n",
       "Epoch 211/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0270 - mae: 0.0270epoch 210: loss: 0.028, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0276 - mae: 0.0276 - val_loss: 0.0603 - val_mae: 0.0603\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0280 - mae: 0.0280 - val_loss: 0.0567 - val_mae: 0.0567\n",
       "Epoch 212/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0270 - mae: 0.0270epoch 211: loss: 0.028, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0284 - mae: 0.0284 - val_loss: 0.0623 - val_mae: 0.0623\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0253 - mae: 0.0253 - val_loss: 0.0498 - val_mae: 0.0498\n",
       "Epoch 213/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0323 - mae: 0.0323epoch 212: loss: 0.032, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0322 - mae: 0.0322 - val_loss: 0.0635 - val_mae: 0.0635\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0280 - mae: 0.0280 - val_loss: 0.0523 - val_mae: 0.0523\n",
       "Epoch 214/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0272 - mae: 0.0272epoch 213: loss: 0.028, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0275 - mae: 0.0275 - val_loss: 0.0608 - val_mae: 0.0608\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0296 - mae: 0.0296 - val_loss: 0.0572 - val_mae: 0.0572\n",
       "Epoch 215/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0285 - mae: 0.0285epoch 214: loss: 0.028, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0278 - mae: 0.0278 - val_loss: 0.0565 - val_mae: 0.0565\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0294 - mae: 0.0294 - val_loss: 0.0538 - val_mae: 0.0538\n",
       "Epoch 216/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0253 - mae: 0.0253epoch 215: loss: 0.025, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0245 - mae: 0.0245 - val_loss: 0.0557 - val_mae: 0.0557\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0258 - mae: 0.0258 - val_loss: 0.0506 - val_mae: 0.0506\n",
       "Epoch 217/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0255 - mae: 0.0255epoch 216: loss: 0.025, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0252 - mae: 0.0252 - val_loss: 0.0558 - val_mae: 0.0558\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0280 - mae: 0.0280 - val_loss: 0.0577 - val_mae: 0.0577\n",
       "Epoch 218/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0260 - mae: 0.0260epoch 217: loss: 0.027, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0266 - mae: 0.0266 - val_loss: 0.0610 - val_mae: 0.0610\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0289 - mae: 0.0289 - val_loss: 0.0520 - val_mae: 0.0520\n",
       "Epoch 219/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0259 - mae: 0.0259epoch 218: loss: 0.026, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0258 - mae: 0.0258 - val_loss: 0.0540 - val_mae: 0.0540\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0251 - mae: 0.0251 - val_loss: 0.0500 - val_mae: 0.0500\n",
       "Epoch 220/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0246 - mae: 0.0246epoch 219: loss: 0.025, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0246 - mae: 0.0246 - val_loss: 0.0547 - val_mae: 0.0547\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0275 - mae: 0.0275 - val_loss: 0.0511 - val_mae: 0.0511\n",
       "Epoch 221/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0254 - mae: 0.0254epoch 220: loss: 0.026, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0261 - mae: 0.0261 - val_loss: 0.0586 - val_mae: 0.0586\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0280 - mae: 0.0280 - val_loss: 0.0619 - val_mae: 0.0619\n",
       "Epoch 222/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.0241 - mae: 0.0241epoch 221: loss: 0.025, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0255 - mae: 0.0255 - val_loss: 0.0551 - val_mae: 0.0551\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0258 - mae: 0.0258 - val_loss: 0.0530 - val_mae: 0.0530\n",
       "Epoch 223/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0284 - mae: 0.0284epoch 222: loss: 0.027, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0274 - mae: 0.0274 - val_loss: 0.0548 - val_mae: 0.0548\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0283 - mae: 0.0283 - val_loss: 0.0524 - val_mae: 0.0524\n",
       "Epoch 224/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0253 - mae: 0.0253epoch 223: loss: 0.025, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0249 - mae: 0.0249 - val_loss: 0.0582 - val_mae: 0.0582\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0302 - mae: 0.0302 - val_loss: 0.0504 - val_mae: 0.0504\n",
       "Epoch 225/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0296 - mae: 0.0296epoch 224: loss: 0.029, val_loss:0.068 val_mae:0.068\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0293 - mae: 0.0293 - val_loss: 0.0678 - val_mae: 0.0678\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0253 - mae: 0.0253 - val_loss: 0.0507 - val_mae: 0.0507\n",
       "Epoch 226/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0311 - mae: 0.0311epoch 225: loss: 0.029, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0293 - mae: 0.0293 - val_loss: 0.0570 - val_mae: 0.0570\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0265 - mae: 0.0265 - val_loss: 0.0484 - val_mae: 0.0484\n",
       "Epoch 227/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0268 - mae: 0.0268epoch 226: loss: 0.026, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0259 - mae: 0.0259 - val_loss: 0.0534 - val_mae: 0.0534\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0269 - mae: 0.0269 - val_loss: 0.0554 - val_mae: 0.0554\n",
       "Epoch 228/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0276 - mae: 0.0276epoch 227: loss: 0.027, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0272 - mae: 0.0272 - val_loss: 0.0555 - val_mae: 0.0555\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0315 - mae: 0.0315 - val_loss: 0.0529 - val_mae: 0.0529\n",
       "Epoch 229/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0288 - mae: 0.0288epoch 228: loss: 0.029, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0291 - mae: 0.0291 - val_loss: 0.0572 - val_mae: 0.0572\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0306 - mae: 0.0306 - val_loss: 0.0524 - val_mae: 0.0524\n",
       "Epoch 230/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0283 - mae: 0.0283epoch 229: loss: 0.028, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0282 - mae: 0.0282 - val_loss: 0.0532 - val_mae: 0.0532\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0277 - mae: 0.0277 - val_loss: 0.0524 - val_mae: 0.0524\n",
       "Epoch 231/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0281 - mae: 0.0281epoch 230: loss: 0.028, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0276 - mae: 0.0276 - val_loss: 0.0547 - val_mae: 0.0547\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0265 - mae: 0.0265 - val_loss: 0.0529 - val_mae: 0.0529\n",
       "Epoch 232/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0261 - mae: 0.0261epoch 231: loss: 0.026, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0258 - mae: 0.0258 - val_loss: 0.0551 - val_mae: 0.0551\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0337 - mae: 0.0337 - val_loss: 0.0620 - val_mae: 0.0620\n",
       "Epoch 233/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0254 - mae: 0.0254epoch 232: loss: 0.025, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0253 - mae: 0.0253 - val_loss: 0.0533 - val_mae: 0.0533\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0265 - mae: 0.0265 - val_loss: 0.0517 - val_mae: 0.0517\n",
       "Epoch 234/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0214 - mae: 0.0214epoch 233: loss: 0.022, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0220 - mae: 0.0220 - val_loss: 0.0640 - val_mae: 0.0640\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0252 - mae: 0.0252 - val_loss: 0.0497 - val_mae: 0.0497\n",
       "Epoch 235/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0250 - mae: 0.0250epoch 234: loss: 0.025, val_loss:0.067 val_mae:0.067\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0252 - mae: 0.0252 - val_loss: 0.0669 - val_mae: 0.0669\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0243 - mae: 0.0243 - val_loss: 0.0510 - val_mae: 0.0510\n",
       "Epoch 236/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0243 - mae: 0.0243epoch 235: loss: 0.024, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0245 - mae: 0.0245 - val_loss: 0.0548 - val_mae: 0.0548\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0258 - mae: 0.0258 - val_loss: 0.0479 - val_mae: 0.0479\n",
       "Epoch 237/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0286 - mae: 0.0286epoch 236: loss: 0.028, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0277 - mae: 0.0277 - val_loss: 0.0523 - val_mae: 0.0523\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0238 - mae: 0.0238 - val_loss: 0.0504 - val_mae: 0.0504\n",
       "Epoch 238/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0219 - mae: 0.0219epoch 237: loss: 0.024, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0240 - mae: 0.0240 - val_loss: 0.0638 - val_mae: 0.0638\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0242 - mae: 0.0242 - val_loss: 0.0488 - val_mae: 0.0488\n",
       "Epoch 239/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0273 - mae: 0.0273epoch 238: loss: 0.027, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0271 - mae: 0.0271 - val_loss: 0.0545 - val_mae: 0.0545\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0236 - mae: 0.0236 - val_loss: 0.0554 - val_mae: 0.0554\n",
       "Epoch 240/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.0215 - mae: 0.0215epoch 239: loss: 0.021, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0214 - mae: 0.0214 - val_loss: 0.0594 - val_mae: 0.0594\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0244 - mae: 0.0244 - val_loss: 0.0527 - val_mae: 0.0527\n",
       "Epoch 241/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0236 - mae: 0.0236epoch 240: loss: 0.024, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0241 - mae: 0.0241 - val_loss: 0.0598 - val_mae: 0.0598\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0268 - mae: 0.0268 - val_loss: 0.0540 - val_mae: 0.0540\n",
       "Epoch 242/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0253 - mae: 0.0253epoch 241: loss: 0.025, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0254 - mae: 0.0254 - val_loss: 0.0561 - val_mae: 0.0561\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0251 - mae: 0.0251 - val_loss: 0.0514 - val_mae: 0.0514\n",
       "Epoch 243/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.0244 - mae: 0.0244epoch 242: loss: 0.025, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0254 - mae: 0.0254 - val_loss: 0.0623 - val_mae: 0.0623\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0250 - mae: 0.0250 - val_loss: 0.0527 - val_mae: 0.0527\n",
       "Epoch 244/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0262 - mae: 0.0262epoch 243: loss: 0.026, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0261 - mae: 0.0261 - val_loss: 0.0539 - val_mae: 0.0539\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0257 - mae: 0.0257 - val_loss: 0.0518 - val_mae: 0.0518\n",
       "Epoch 245/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0227 - mae: 0.0227epoch 244: loss: 0.023, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0225 - mae: 0.0225 - val_loss: 0.0541 - val_mae: 0.0541\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0246 - mae: 0.0246 - val_loss: 0.0534 - val_mae: 0.0534\n",
       "Epoch 246/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0256 - mae: 0.0256epoch 245: loss: 0.026, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0256 - mae: 0.0256 - val_loss: 0.0543 - val_mae: 0.0543\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0261 - mae: 0.0261 - val_loss: 0.0558 - val_mae: 0.0558\n",
       "Epoch 247/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0283 - mae: 0.0283epoch 246: loss: 0.028, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0281 - mae: 0.0281 - val_loss: 0.0541 - val_mae: 0.0541\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0238 - mae: 0.0238 - val_loss: 0.0535 - val_mae: 0.0535\n",
       "Epoch 248/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0254 - mae: 0.0254epoch 247: loss: 0.024, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0245 - mae: 0.0245 - val_loss: 0.0516 - val_mae: 0.0516\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0259 - mae: 0.0259 - val_loss: 0.0535 - val_mae: 0.0535\n",
       "Epoch 249/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0266 - mae: 0.0266epoch 248: loss: 0.027, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0272 - mae: 0.0272 - val_loss: 0.0533 - val_mae: 0.0533\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0259 - mae: 0.0259 - val_loss: 0.0498 - val_mae: 0.0498\n",
       "Epoch 250/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0293 - mae: 0.0293epoch 249: loss: 0.028, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0283 - mae: 0.0283 - val_loss: 0.0521 - val_mae: 0.0521\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0228 - mae: 0.0228 - val_loss: 0.0492 - val_mae: 0.0492\n",
       "Epoch 251/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0234 - mae: 0.0234epoch 250: loss: 0.024, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0242 - mae: 0.0242 - val_loss: 0.0527 - val_mae: 0.0527\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0228 - mae: 0.0228 - val_loss: 0.0501 - val_mae: 0.0501\n",
       "Epoch 252/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0223 - mae: 0.0223epoch 251: loss: 0.023, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0232 - mae: 0.0232 - val_loss: 0.0567 - val_mae: 0.0567\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0271 - mae: 0.0271 - val_loss: 0.0501 - val_mae: 0.0501\n",
       "Epoch 253/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0222 - mae: 0.0222epoch 252: loss: 0.023, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0229 - mae: 0.0229 - val_loss: 0.0592 - val_mae: 0.0592\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0251 - mae: 0.0251 - val_loss: 0.0516 - val_mae: 0.0516\n",
       "Epoch 254/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0252 - mae: 0.0252epoch 253: loss: 0.025, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0250 - mae: 0.0250 - val_loss: 0.0542 - val_mae: 0.0542\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0222 - mae: 0.0222 - val_loss: 0.0472 - val_mae: 0.0472\n",
       "Epoch 255/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0248 - mae: 0.0248epoch 254: loss: 0.025, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0253 - mae: 0.0253 - val_loss: 0.0555 - val_mae: 0.0555\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0222 - mae: 0.0222 - val_loss: 0.0530 - val_mae: 0.0530\n",
       "Epoch 256/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0248 - mae: 0.0248epoch 255: loss: 0.025, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0249 - mae: 0.0249 - val_loss: 0.0531 - val_mae: 0.0531\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0239 - mae: 0.0239 - val_loss: 0.0519 - val_mae: 0.0519\n",
       "Epoch 257/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0225 - mae: 0.0225epoch 256: loss: 0.023, val_loss:0.051 val_mae:0.051\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0229 - mae: 0.0229 - val_loss: 0.0508 - val_mae: 0.0508\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0237 - mae: 0.0237 - val_loss: 0.0538 - val_mae: 0.0538\n",
       "Epoch 258/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0211 - mae: 0.0211epoch 257: loss: 0.021, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0208 - mae: 0.0208 - val_loss: 0.0522 - val_mae: 0.0522\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0247 - mae: 0.0247 - val_loss: 0.0541 - val_mae: 0.0541\n",
       "Epoch 259/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0211 - mae: 0.0211epoch 258: loss: 0.021, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0213 - mae: 0.0213 - val_loss: 0.0546 - val_mae: 0.0546\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0260 - mae: 0.0260 - val_loss: 0.0499 - val_mae: 0.0499\n",
       "Epoch 260/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0212 - mae: 0.0212epoch 259: loss: 0.021, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0209 - mae: 0.0209 - val_loss: 0.0570 - val_mae: 0.0570\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0232 - mae: 0.0232 - val_loss: 0.0517 - val_mae: 0.0517\n",
       "Epoch 261/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0263 - mae: 0.0263epoch 260: loss: 0.025, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0250 - mae: 0.0250 - val_loss: 0.0548 - val_mae: 0.0548\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0229 - mae: 0.0229 - val_loss: 0.0483 - val_mae: 0.0483\n",
       "Epoch 262/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0206 - mae: 0.0206epoch 261: loss: 0.020, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0204 - mae: 0.0204 - val_loss: 0.0598 - val_mae: 0.0598\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0233 - mae: 0.0233 - val_loss: 0.0522 - val_mae: 0.0522\n",
       "Epoch 263/300\n",
-      "41/57 [====================>.........] - ETA: 0s - loss: 0.0286 - mae: 0.0286epoch 262: loss: 0.028, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0277 - mae: 0.0277 - val_loss: 0.0581 - val_mae: 0.0581\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0233 - mae: 0.0233 - val_loss: 0.0524 - val_mae: 0.0524\n",
       "Epoch 264/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0284 - mae: 0.0284epoch 263: loss: 0.027, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0267 - mae: 0.0267 - val_loss: 0.0541 - val_mae: 0.0541\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0261 - mae: 0.0261 - val_loss: 0.0535 - val_mae: 0.0535\n",
       "Epoch 265/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0259 - mae: 0.0259epoch 264: loss: 0.026, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0263 - mae: 0.0263 - val_loss: 0.0543 - val_mae: 0.0543\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0280 - mae: 0.0280 - val_loss: 0.0522 - val_mae: 0.0522\n",
       "Epoch 266/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0218 - mae: 0.0218epoch 265: loss: 0.022, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0217 - mae: 0.0217 - val_loss: 0.0543 - val_mae: 0.0543\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0272 - mae: 0.0272 - val_loss: 0.0551 - val_mae: 0.0551\n",
       "Epoch 267/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0238 - mae: 0.0238epoch 266: loss: 0.024, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0242 - mae: 0.0242 - val_loss: 0.0525 - val_mae: 0.0525\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0242 - mae: 0.0242 - val_loss: 0.0499 - val_mae: 0.0499\n",
       "Epoch 268/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0263 - mae: 0.0263epoch 267: loss: 0.027, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0270 - mae: 0.0270 - val_loss: 0.0582 - val_mae: 0.0582\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0230 - mae: 0.0230 - val_loss: 0.0505 - val_mae: 0.0505\n",
       "Epoch 269/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0227 - mae: 0.0227epoch 268: loss: 0.023, val_loss:0.061 val_mae:0.061\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0230 - mae: 0.0230 - val_loss: 0.0606 - val_mae: 0.0606\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0229 - mae: 0.0229 - val_loss: 0.0477 - val_mae: 0.0477\n",
       "Epoch 270/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0232 - mae: 0.0232epoch 269: loss: 0.023, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0229 - mae: 0.0229 - val_loss: 0.0594 - val_mae: 0.0594\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0221 - mae: 0.0221 - val_loss: 0.0505 - val_mae: 0.0505\n",
       "Epoch 271/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0238 - mae: 0.0238epoch 270: loss: 0.024, val_loss:0.064 val_mae:0.064\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0241 - mae: 0.0241 - val_loss: 0.0636 - val_mae: 0.0636\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0245 - mae: 0.0245 - val_loss: 0.0498 - val_mae: 0.0498\n",
       "Epoch 272/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0237 - mae: 0.0237epoch 271: loss: 0.023, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0231 - mae: 0.0231 - val_loss: 0.0543 - val_mae: 0.0543\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0215 - mae: 0.0215 - val_loss: 0.0519 - val_mae: 0.0519\n",
       "Epoch 273/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0218 - mae: 0.0218epoch 272: loss: 0.021, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0215 - mae: 0.0215 - val_loss: 0.0520 - val_mae: 0.0520\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0265 - mae: 0.0265 - val_loss: 0.0517 - val_mae: 0.0517\n",
       "Epoch 274/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0216 - mae: 0.0216epoch 273: loss: 0.022, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0218 - mae: 0.0218 - val_loss: 0.0588 - val_mae: 0.0588\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0237 - mae: 0.0237 - val_loss: 0.0566 - val_mae: 0.0566\n",
       "Epoch 275/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0239 - mae: 0.0239epoch 274: loss: 0.023, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0232 - mae: 0.0232 - val_loss: 0.0545 - val_mae: 0.0545\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0235 - mae: 0.0235 - val_loss: 0.0484 - val_mae: 0.0484\n",
       "Epoch 276/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0208 - mae: 0.0208epoch 275: loss: 0.022, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0219 - mae: 0.0219 - val_loss: 0.0529 - val_mae: 0.0529\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0213 - mae: 0.0213 - val_loss: 0.0488 - val_mae: 0.0488\n",
       "Epoch 277/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0210 - mae: 0.0210epoch 276: loss: 0.021, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0213 - mae: 0.0213 - val_loss: 0.0567 - val_mae: 0.0567\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0221 - mae: 0.0221 - val_loss: 0.0551 - val_mae: 0.0551\n",
       "Epoch 278/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0226 - mae: 0.0226epoch 277: loss: 0.023, val_loss:0.058 val_mae:0.058\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0230 - mae: 0.0230 - val_loss: 0.0581 - val_mae: 0.0581\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0255 - mae: 0.0255 - val_loss: 0.0489 - val_mae: 0.0489\n",
       "Epoch 279/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0234 - mae: 0.0234epoch 278: loss: 0.022, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0223 - mae: 0.0223 - val_loss: 0.0536 - val_mae: 0.0536\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0204 - mae: 0.0204 - val_loss: 0.0493 - val_mae: 0.0493\n",
       "Epoch 280/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0196 - mae: 0.0196epoch 279: loss: 0.019, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0524 - val_mae: 0.0524\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0199 - mae: 0.0199 - val_loss: 0.0474 - val_mae: 0.0474\n",
       "Epoch 281/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0242 - mae: 0.0242epoch 280: loss: 0.025, val_loss:0.062 val_mae:0.062\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0247 - mae: 0.0247 - val_loss: 0.0624 - val_mae: 0.0624\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0198 - mae: 0.0198 - val_loss: 0.0517 - val_mae: 0.0517\n",
       "Epoch 282/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0308 - mae: 0.0308epoch 281: loss: 0.030, val_loss:0.054 val_mae:0.054\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0301 - mae: 0.0301 - val_loss: 0.0540 - val_mae: 0.0540\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0221 - mae: 0.0221 - val_loss: 0.0471 - val_mae: 0.0471\n",
       "Epoch 283/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0216 - mae: 0.0216epoch 282: loss: 0.022, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0220 - mae: 0.0220 - val_loss: 0.0568 - val_mae: 0.0568\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0230 - mae: 0.0230 - val_loss: 0.0507 - val_mae: 0.0507\n",
       "Epoch 284/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0225 - mae: 0.0225epoch 283: loss: 0.023, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0227 - mae: 0.0227 - val_loss: 0.0551 - val_mae: 0.0551\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0231 - mae: 0.0231 - val_loss: 0.0480 - val_mae: 0.0480\n",
       "Epoch 285/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0226 - mae: 0.0226epoch 284: loss: 0.022, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0220 - mae: 0.0220 - val_loss: 0.0530 - val_mae: 0.0530\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0220 - mae: 0.0220 - val_loss: 0.0510 - val_mae: 0.0510\n",
       "Epoch 286/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0223 - mae: 0.0223epoch 285: loss: 0.023, val_loss:0.060 val_mae:0.060\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0227 - mae: 0.0227 - val_loss: 0.0595 - val_mae: 0.0595\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0244 - mae: 0.0244 - val_loss: 0.0525 - val_mae: 0.0525\n",
       "Epoch 287/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0225 - mae: 0.0225epoch 286: loss: 0.023, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0231 - mae: 0.0231 - val_loss: 0.0551 - val_mae: 0.0551\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0249 - mae: 0.0249 - val_loss: 0.0590 - val_mae: 0.0590\n",
       "Epoch 288/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0246 - mae: 0.0246epoch 287: loss: 0.024, val_loss:0.055 val_mae:0.055\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0239 - mae: 0.0239 - val_loss: 0.0554 - val_mae: 0.0554\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0248 - mae: 0.0248 - val_loss: 0.0614 - val_mae: 0.0614\n",
       "Epoch 289/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0230 - mae: 0.0230epoch 288: loss: 0.023, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0229 - mae: 0.0229 - val_loss: 0.0525 - val_mae: 0.0525\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0233 - mae: 0.0233 - val_loss: 0.0491 - val_mae: 0.0491\n",
       "Epoch 290/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0211 - mae: 0.0211epoch 289: loss: 0.021, val_loss:0.050 val_mae:0.050\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0209 - mae: 0.0209 - val_loss: 0.0505 - val_mae: 0.0505\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0221 - mae: 0.0221 - val_loss: 0.0528 - val_mae: 0.0528\n",
       "Epoch 291/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0201 - mae: 0.0201epoch 290: loss: 0.021, val_loss:0.051 val_mae:0.051\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0207 - mae: 0.0207 - val_loss: 0.0512 - val_mae: 0.0512\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0194 - mae: 0.0194 - val_loss: 0.0480 - val_mae: 0.0480\n",
       "Epoch 292/300\n",
-      "42/57 [=====================>........] - ETA: 0s - loss: 0.0215 - mae: 0.0215epoch 291: loss: 0.021, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 2ms/step - loss: 0.0211 - mae: 0.0211 - val_loss: 0.0524 - val_mae: 0.0524\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0273 - mae: 0.0273 - val_loss: 0.0527 - val_mae: 0.0527\n",
       "Epoch 293/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0204 - mae: 0.0204epoch 292: loss: 0.020, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0196 - mae: 0.0196 - val_loss: 0.0525 - val_mae: 0.0525\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0225 - mae: 0.0225 - val_loss: 0.0498 - val_mae: 0.0498\n",
       "Epoch 294/300\n",
-      "45/57 [======================>.......] - ETA: 0s - loss: 0.0212 - mae: 0.0212epoch 293: loss: 0.022, val_loss:0.056 val_mae:0.056\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0224 - mae: 0.0224 - val_loss: 0.0559 - val_mae: 0.0559\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0219 - mae: 0.0219 - val_loss: 0.0542 - val_mae: 0.0542\n",
       "Epoch 295/300\n",
-      "46/57 [=======================>......] - ETA: 0s - loss: 0.0220 - mae: 0.0220epoch 294: loss: 0.021, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0214 - mae: 0.0214 - val_loss: 0.0524 - val_mae: 0.0524\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0201 - mae: 0.0201 - val_loss: 0.0478 - val_mae: 0.0478\n",
       "Epoch 296/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0231 - mae: 0.0231epoch 295: loss: 0.023, val_loss:0.052 val_mae:0.052\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0228 - mae: 0.0228 - val_loss: 0.0520 - val_mae: 0.0520\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0189 - mae: 0.0189 - val_loss: 0.0495 - val_mae: 0.0495\n",
       "Epoch 297/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0211 - mae: 0.0211epoch 296: loss: 0.022, val_loss:0.051 val_mae:0.051\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0222 - mae: 0.0222 - val_loss: 0.0514 - val_mae: 0.0514\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0204 - mae: 0.0204 - val_loss: 0.0555 - val_mae: 0.0555\n",
       "Epoch 298/300\n",
-      "43/57 [=====================>........] - ETA: 0s - loss: 0.0197 - mae: 0.0197epoch 297: loss: 0.022, val_loss:0.059 val_mae:0.059\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0218 - mae: 0.0218 - val_loss: 0.0594 - val_mae: 0.0594\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0215 - mae: 0.0215 - val_loss: 0.0500 - val_mae: 0.0500\n",
       "Epoch 299/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0242 - mae: 0.0242epoch 298: loss: 0.023, val_loss:0.053 val_mae:0.053\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0231 - mae: 0.0231 - val_loss: 0.0528 - val_mae: 0.0528\n",
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0222 - mae: 0.0222 - val_loss: 0.0492 - val_mae: 0.0492\n",
       "Epoch 300/300\n",
-      "44/57 [======================>.......] - ETA: 0s - loss: 0.0202 - mae: 0.0202epoch 299: loss: 0.022, val_loss:0.057 val_mae:0.057\n",
-      "57/57 [==============================] - 0s 1ms/step - loss: 0.0216 - mae: 0.0216 - val_loss: 0.0567 - val_mae: 0.0567\n"
+      "57/57 [==============================] - 0s 1ms/step - loss: 0.0230 - mae: 0.0230 - val_loss: 0.0493 - val_mae: 0.0493\n"
      ]
     }
    ],
    "source": [
-    "#md.shuffle()\n",
-    "model.fit(md,val_fraction=0.1, val_key='refractive_index', loss='mae', lr=0.001, epochs = 300, batch_size = 64, xscale='minmax',yscale=None, verbose=1)"
+    "model.fit(md,val_fraction=0.1,\n",
+    "          val_key='refractive_index',\n",
+    "          loss='mae', lr=0.001, epochs = 300,\n",
+    "          batch_size = 64, xscale='minmax',\n",
+    "          yscale=None,\n",
+    "          verbose=1\n",
+    "         )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Compiling model...\n",
-      "INFO:root:Fitting model...\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0011 - mae: 0.0270epoch 0: loss: 0.002, val_loss:0.010 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 5ms/step - loss: 0.0018 - mae: 0.0293 - val_loss: 0.0095 - val_mae: 0.0505\n",
+      "29/29 [==============================] - 0s 4ms/step - loss: 0.0016 - mae: 0.0257 - val_loss: 0.0078 - val_mae: 0.0464\n",
       "Epoch 2/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 4.2293e-04 - mae: 0.0157epoch 1: loss: 0.001, val_loss:0.009 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 6.9871e-04 - mae: 0.0145 - val_loss: 0.0091 - val_mae: 0.0503\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 7.4995e-04 - mae: 0.0153 - val_loss: 0.0080 - val_mae: 0.0458\n",
       "Epoch 3/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.4598e-04 - mae: 0.0123epoch 2: loss: 0.001, val_loss:0.009 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.7062e-04 - mae: 0.0126 - val_loss: 0.0092 - val_mae: 0.0503\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 6.0186e-04 - mae: 0.0130 - val_loss: 0.0079 - val_mae: 0.0464\n",
       "Epoch 4/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.9102e-04 - mae: 0.0110epoch 3: loss: 0.001, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.6609e-04 - mae: 0.0135 - val_loss: 0.0093 - val_mae: 0.0517\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.4298e-04 - mae: 0.0118 - val_loss: 0.0082 - val_mae: 0.0472\n",
       "Epoch 5/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.3980e-04 - mae: 0.0123epoch 4: loss: 0.001, val_loss:0.009 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.0947e-04 - mae: 0.0118 - val_loss: 0.0093 - val_mae: 0.0505\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 6.1070e-04 - mae: 0.0139 - val_loss: 0.0076 - val_mae: 0.0463\n",
       "Epoch 6/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0029 - mae: 0.0171epoch 5: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.9527e-04 - mae: 0.0120 - val_loss: 0.0094 - val_mae: 0.0506\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 6.1499e-04 - mae: 0.0140 - val_loss: 0.0081 - val_mae: 0.0479\n",
       "Epoch 7/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.8389e-04 - mae: 0.0123epoch 6: loss: 0.000, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.5512e-04 - mae: 0.0109 - val_loss: 0.0098 - val_mae: 0.0532\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.4536e-04 - mae: 0.0131 - val_loss: 0.0079 - val_mae: 0.0463\n",
       "Epoch 8/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.4838e-04 - mae: 0.0142epoch 7: loss: 0.001, val_loss:0.009 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.7610e-04 - mae: 0.0142 - val_loss: 0.0092 - val_mae: 0.0499\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.4517e-04 - mae: 0.0134 - val_loss: 0.0080 - val_mae: 0.0469\n",
       "Epoch 9/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 4.2091e-04 - mae: 0.0123epoch 8: loss: 0.001, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.5291e-04 - mae: 0.0131 - val_loss: 0.0098 - val_mae: 0.0514\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.4248e-04 - mae: 0.0128 - val_loss: 0.0078 - val_mae: 0.0459\n",
       "Epoch 10/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.5551e-04 - mae: 0.0122epoch 9: loss: 0.001, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.6515e-04 - mae: 0.0139 - val_loss: 0.0093 - val_mae: 0.0509\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.2851e-04 - mae: 0.0122 - val_loss: 0.0083 - val_mae: 0.0491\n",
       "Epoch 11/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0010 - mae: 0.0128epoch 10: loss: 0.000, val_loss:0.010 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.8372e-04 - mae: 0.0122 - val_loss: 0.0095 - val_mae: 0.0501\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 6.7202e-04 - mae: 0.0157 - val_loss: 0.0085 - val_mae: 0.0476\n",
       "Epoch 12/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0010 - mae: 0.0147epoch 11: loss: 0.001, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.1137e-04 - mae: 0.0131 - val_loss: 0.0091 - val_mae: 0.0509\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 6.0778e-04 - mae: 0.0144 - val_loss: 0.0081 - val_mae: 0.0475\n",
       "Epoch 13/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.2365e-04 - mae: 0.0107epoch 12: loss: 0.000, val_loss:0.009 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.6050e-04 - mae: 0.0119 - val_loss: 0.0091 - val_mae: 0.0500\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.9792e-04 - mae: 0.0140 - val_loss: 0.0081 - val_mae: 0.0479\n",
       "Epoch 14/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.0252e-04 - mae: 0.0097epoch 13: loss: 0.001, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.6375e-04 - mae: 0.0140 - val_loss: 0.0092 - val_mae: 0.0513\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.9998e-04 - mae: 0.0139 - val_loss: 0.0080 - val_mae: 0.0500\n",
       "Epoch 15/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.4898e-04 - mae: 0.0125epoch 14: loss: 0.001, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.0984e-04 - mae: 0.0131 - val_loss: 0.0094 - val_mae: 0.0507\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.7723e-04 - mae: 0.0143 - val_loss: 0.0092 - val_mae: 0.0490\n",
       "Epoch 16/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.1145e-04 - mae: 0.0103epoch 15: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.0780e-04 - mae: 0.0109 - val_loss: 0.0097 - val_mae: 0.0514\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.3562e-04 - mae: 0.0136 - val_loss: 0.0081 - val_mae: 0.0468\n",
       "Epoch 17/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.6838e-04 - mae: 0.0110epoch 16: loss: 0.000, val_loss:0.009 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.8801e-04 - mae: 0.0137 - val_loss: 0.0094 - val_mae: 0.0503\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 4.8906e-04 - mae: 0.0128 - val_loss: 0.0081 - val_mae: 0.0474\n",
       "Epoch 18/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0019 - mae: 0.0137epoch 17: loss: 0.001, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.0025e-04 - mae: 0.0131 - val_loss: 0.0098 - val_mae: 0.0529\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 7.1883e-04 - mae: 0.0173 - val_loss: 0.0083 - val_mae: 0.0488\n",
       "Epoch 19/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0012 - mae: 0.0204epoch 18: loss: 0.001, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 6.1675e-04 - mae: 0.0152 - val_loss: 0.0094 - val_mae: 0.0507\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.6701e-04 - mae: 0.0152 - val_loss: 0.0085 - val_mae: 0.0477\n",
       "Epoch 20/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 4.4805e-04 - mae: 0.0161epoch 19: loss: 0.001, val_loss:0.010 val_mae:0.058\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.8473e-04 - mae: 0.0148 - val_loss: 0.0102 - val_mae: 0.0580\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.2419e-04 - mae: 0.0138 - val_loss: 0.0087 - val_mae: 0.0524\n",
       "Epoch 21/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 8.4729e-04 - mae: 0.0236epoch 20: loss: 0.001, val_loss:0.010 val_mae:0.055\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 0.0011 - mae: 0.0230 - val_loss: 0.0096 - val_mae: 0.0549\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 7.7548e-04 - mae: 0.0188 - val_loss: 0.0080 - val_mae: 0.0473\n",
       "Epoch 22/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 7.7041e-04 - mae: 0.0213epoch 21: loss: 0.001, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 6.8664e-04 - mae: 0.0165 - val_loss: 0.0100 - val_mae: 0.0511\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 6.0016e-04 - mae: 0.0161 - val_loss: 0.0083 - val_mae: 0.0464\n",
       "Epoch 23/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.2364e-04 - mae: 0.0112epoch 22: loss: 0.001, val_loss:0.010 val_mae:0.056\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 6.2281e-04 - mae: 0.0162 - val_loss: 0.0102 - val_mae: 0.0556\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 4.5809e-04 - mae: 0.0130 - val_loss: 0.0081 - val_mae: 0.0471\n",
       "Epoch 24/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 8.9509e-04 - mae: 0.0243epoch 23: loss: 0.001, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 6.1376e-04 - mae: 0.0167 - val_loss: 0.0095 - val_mae: 0.0518\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 4.0795e-04 - mae: 0.0124 - val_loss: 0.0081 - val_mae: 0.0470\n",
       "Epoch 25/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.8678e-04 - mae: 0.0128epoch 24: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.6426e-04 - mae: 0.0111 - val_loss: 0.0092 - val_mae: 0.0510\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 4.2724e-04 - mae: 0.0132 - val_loss: 0.0082 - val_mae: 0.0485\n",
       "Epoch 26/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.5033e-04 - mae: 0.0107epoch 25: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.7319e-04 - mae: 0.0116 - val_loss: 0.0093 - val_mae: 0.0507\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 4.5458e-04 - mae: 0.0136 - val_loss: 0.0098 - val_mae: 0.0569\n",
       "Epoch 27/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 9.6387e-04 - mae: 0.0125epoch 26: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.4608e-04 - mae: 0.0109 - val_loss: 0.0093 - val_mae: 0.0521\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0100 - val_mae: 0.0542\n",
       "Epoch 28/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0018 - mae: 0.0151epoch 27: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.4063e-04 - mae: 0.0135 - val_loss: 0.0094 - val_mae: 0.0516\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 5.3668e-04 - mae: 0.0156 - val_loss: 0.0084 - val_mae: 0.0477\n",
       "Epoch 29/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.8061e-04 - mae: 0.0102epoch 28: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.6871e-04 - mae: 0.0114 - val_loss: 0.0095 - val_mae: 0.0508\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.6246e-04 - mae: 0.0120 - val_loss: 0.0084 - val_mae: 0.0475\n",
       "Epoch 30/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.6089e-04 - mae: 0.0110epoch 29: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.3813e-04 - mae: 0.0108 - val_loss: 0.0095 - val_mae: 0.0509\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.2207e-04 - mae: 0.0114 - val_loss: 0.0080 - val_mae: 0.0459\n",
       "Epoch 31/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.9423e-04 - mae: 0.0108epoch 30: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.7099e-04 - mae: 0.0119 - val_loss: 0.0093 - val_mae: 0.0515\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.0498e-04 - mae: 0.0107 - val_loss: 0.0081 - val_mae: 0.0460\n",
       "Epoch 32/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.4781e-04 - mae: 0.0098epoch 31: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.0404e-04 - mae: 0.0103 - val_loss: 0.0097 - val_mae: 0.0515\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.3488e-04 - mae: 0.0117 - val_loss: 0.0086 - val_mae: 0.0496\n",
       "Epoch 33/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.3179e-04 - mae: 0.0120epoch 32: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.3720e-04 - mae: 0.0115 - val_loss: 0.0096 - val_mae: 0.0521\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.4400e-04 - mae: 0.0125 - val_loss: 0.0079 - val_mae: 0.0463\n",
       "Epoch 34/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.2053e-04 - mae: 0.0124epoch 33: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.0966e-04 - mae: 0.0104 - val_loss: 0.0096 - val_mae: 0.0512\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 4.4541e-04 - mae: 0.0141 - val_loss: 0.0081 - val_mae: 0.0469\n",
       "Epoch 35/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.7297e-04 - mae: 0.0093epoch 34: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.5312e-04 - mae: 0.0134 - val_loss: 0.0096 - val_mae: 0.0507\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 4.1514e-04 - mae: 0.0136 - val_loss: 0.0085 - val_mae: 0.0464\n",
       "Epoch 36/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 5.0654e-04 - mae: 0.0121epoch 35: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.2081e-04 - mae: 0.0128 - val_loss: 0.0094 - val_mae: 0.0520\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.2712e-04 - mae: 0.0115 - val_loss: 0.0080 - val_mae: 0.0470\n",
       "Epoch 37/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.6756e-04 - mae: 0.0126epoch 36: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.3410e-04 - mae: 0.0110 - val_loss: 0.0096 - val_mae: 0.0516\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.2071e-04 - mae: 0.0117 - val_loss: 0.0083 - val_mae: 0.0465\n",
       "Epoch 38/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.6258e-04 - mae: 0.0097epoch 37: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.8600e-04 - mae: 0.0122 - val_loss: 0.0096 - val_mae: 0.0517\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.0874e-04 - mae: 0.0116 - val_loss: 0.0081 - val_mae: 0.0477\n",
       "Epoch 39/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.6579e-04 - mae: 0.0103epoch 38: loss: 0.000, val_loss:0.009 val_mae:0.054\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.6172e-04 - mae: 0.0114 - val_loss: 0.0095 - val_mae: 0.0537\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.1487e-04 - mae: 0.0118 - val_loss: 0.0083 - val_mae: 0.0482\n",
       "Epoch 40/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0016 - mae: 0.0182epoch 39: loss: 0.001, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.5578e-04 - mae: 0.0149 - val_loss: 0.0098 - val_mae: 0.0528\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.8506e-04 - mae: 0.0109 - val_loss: 0.0080 - val_mae: 0.0466\n",
       "Epoch 41/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0016 - mae: 0.0182epoch 40: loss: 0.001, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.3224e-04 - mae: 0.0146 - val_loss: 0.0096 - val_mae: 0.0508\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.6638e-04 - mae: 0.0130 - val_loss: 0.0085 - val_mae: 0.0482\n",
       "Epoch 42/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.8929e-04 - mae: 0.0124epoch 41: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.9502e-04 - mae: 0.0132 - val_loss: 0.0100 - val_mae: 0.0516\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.9001e-04 - mae: 0.0114 - val_loss: 0.0085 - val_mae: 0.0483\n",
       "Epoch 43/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0017 - mae: 0.0162epoch 42: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.1657e-04 - mae: 0.0136 - val_loss: 0.0098 - val_mae: 0.0520\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.8737e-04 - mae: 0.0111 - val_loss: 0.0082 - val_mae: 0.0467\n",
       "Epoch 44/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.9598e-04 - mae: 0.0094epoch 43: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.0053e-04 - mae: 0.0107 - val_loss: 0.0097 - val_mae: 0.0508\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.0144e-04 - mae: 0.0117 - val_loss: 0.0082 - val_mae: 0.0488\n",
       "Epoch 45/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 0.0010 - mae: 0.0153epoch 44: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.3833e-04 - mae: 0.0123 - val_loss: 0.0095 - val_mae: 0.0516\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.7243e-04 - mae: 0.0110 - val_loss: 0.0081 - val_mae: 0.0480\n",
       "Epoch 46/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 6.5187e-04 - mae: 0.0139epoch 45: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.1389e-04 - mae: 0.0113 - val_loss: 0.0095 - val_mae: 0.0509\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.5300e-04 - mae: 0.0106 - val_loss: 0.0081 - val_mae: 0.0483\n",
       "Epoch 47/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.6198e-04 - mae: 0.0115epoch 46: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.8960e-04 - mae: 0.0105 - val_loss: 0.0098 - val_mae: 0.0520\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.7340e-04 - mae: 0.0111 - val_loss: 0.0080 - val_mae: 0.0468\n",
       "Epoch 48/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.6655e-04 - mae: 0.0102epoch 47: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.8963e-04 - mae: 0.0111 - val_loss: 0.0094 - val_mae: 0.0522\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.1905e-04 - mae: 0.0096 - val_loss: 0.0080 - val_mae: 0.0471\n",
       "Epoch 49/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.9727e-04 - mae: 0.0111epoch 48: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.6818e-04 - mae: 0.0106 - val_loss: 0.0093 - val_mae: 0.0513\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.2437e-04 - mae: 0.0098 - val_loss: 0.0083 - val_mae: 0.0470\n",
       "Epoch 50/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.1258e-04 - mae: 0.0083epoch 49: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.6143e-04 - mae: 0.0103 - val_loss: 0.0097 - val_mae: 0.0512\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.8178e-04 - mae: 0.0136 - val_loss: 0.0089 - val_mae: 0.0497\n",
       "Epoch 51/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.3335e-04 - mae: 0.0109epoch 50: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.6225e-04 - mae: 0.0102 - val_loss: 0.0097 - val_mae: 0.0520\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.6929e-04 - mae: 0.0109 - val_loss: 0.0080 - val_mae: 0.0475\n",
       "Epoch 52/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.3817e-04 - mae: 0.0113epoch 51: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.5237e-04 - mae: 0.0127 - val_loss: 0.0093 - val_mae: 0.0508\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.4461e-04 - mae: 0.0104 - val_loss: 0.0081 - val_mae: 0.0469\n",
       "Epoch 53/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.4879e-04 - mae: 0.0117epoch 52: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.4994e-04 - mae: 0.0101 - val_loss: 0.0097 - val_mae: 0.0520\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.3308e-04 - mae: 0.0101 - val_loss: 0.0081 - val_mae: 0.0469\n",
       "Epoch 54/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.3305e-04 - mae: 0.0113epoch 53: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.6694e-04 - mae: 0.0104 - val_loss: 0.0097 - val_mae: 0.0520\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.1908e-04 - mae: 0.0099 - val_loss: 0.0081 - val_mae: 0.0473\n",
       "Epoch 55/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.4285e-04 - mae: 0.0092epoch 54: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.5914e-04 - mae: 0.0103 - val_loss: 0.0093 - val_mae: 0.0509\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.6319e-04 - mae: 0.0134 - val_loss: 0.0081 - val_mae: 0.0471\n",
       "Epoch 56/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.2419e-04 - mae: 0.0086epoch 55: loss: 0.000, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.8410e-04 - mae: 0.0107 - val_loss: 0.0096 - val_mae: 0.0529\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.4644e-04 - mae: 0.0107 - val_loss: 0.0081 - val_mae: 0.0478\n",
       "Epoch 57/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.2561e-04 - mae: 0.0116epoch 56: loss: 0.000, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.9286e-04 - mae: 0.0132 - val_loss: 0.0097 - val_mae: 0.0528\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.4011e-04 - mae: 0.0106 - val_loss: 0.0084 - val_mae: 0.0468\n",
       "Epoch 58/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 4.2125e-04 - mae: 0.0161epoch 57: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.7955e-04 - mae: 0.0134 - val_loss: 0.0092 - val_mae: 0.0512\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.2594e-04 - mae: 0.0103 - val_loss: 0.0081 - val_mae: 0.0465\n",
       "Epoch 59/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.1389e-04 - mae: 0.0104epoch 58: loss: 0.000, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.7594e-04 - mae: 0.0108 - val_loss: 0.0097 - val_mae: 0.0533\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.1053e-04 - mae: 0.0098 - val_loss: 0.0083 - val_mae: 0.0485\n",
       "Epoch 60/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.2474e-04 - mae: 0.0137epoch 59: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.7374e-04 - mae: 0.0134 - val_loss: 0.0091 - val_mae: 0.0511\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.1942e-04 - mae: 0.0102 - val_loss: 0.0081 - val_mae: 0.0467\n",
       "Epoch 61/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 6.5614e-04 - mae: 0.0132epoch 60: loss: 0.000, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.3596e-04 - mae: 0.0124 - val_loss: 0.0095 - val_mae: 0.0529\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.9936e-04 - mae: 0.0097 - val_loss: 0.0081 - val_mae: 0.0467\n",
       "Epoch 62/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.8969e-04 - mae: 0.0137epoch 61: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.8998e-04 - mae: 0.0111 - val_loss: 0.0094 - val_mae: 0.0525\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.9922e-04 - mae: 0.0097 - val_loss: 0.0080 - val_mae: 0.0479\n",
       "Epoch 63/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.6034e-04 - mae: 0.0121epoch 62: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.8545e-04 - mae: 0.0113 - val_loss: 0.0098 - val_mae: 0.0520\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.1060e-04 - mae: 0.0100 - val_loss: 0.0083 - val_mae: 0.0467\n",
       "Epoch 64/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.8201e-04 - mae: 0.0096epoch 63: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.2107e-04 - mae: 0.0120 - val_loss: 0.0095 - val_mae: 0.0515\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.4037e-04 - mae: 0.0109 - val_loss: 0.0082 - val_mae: 0.0472\n",
       "Epoch 65/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.9990e-04 - mae: 0.0107epoch 64: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.2544e-04 - mae: 0.0117 - val_loss: 0.0092 - val_mae: 0.0521\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.4305e-04 - mae: 0.0109 - val_loss: 0.0086 - val_mae: 0.0492\n",
       "Epoch 66/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 4.5045e-04 - mae: 0.0142epoch 65: loss: 0.000, val_loss:0.010 val_mae:0.054\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.2219e-04 - mae: 0.0137 - val_loss: 0.0104 - val_mae: 0.0541\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 6.2494e-04 - mae: 0.0162 - val_loss: 0.0077 - val_mae: 0.0559\n",
       "Epoch 67/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.4897e-04 - mae: 0.0134epoch 66: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.9379e-04 - mae: 0.0116 - val_loss: 0.0095 - val_mae: 0.0513\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 0.0011 - mae: 0.0229 - val_loss: 0.0086 - val_mae: 0.0501\n",
       "Epoch 68/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.1607e-04 - mae: 0.0113epoch 67: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.9883e-04 - mae: 0.0121 - val_loss: 0.0097 - val_mae: 0.0511\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 8.4277e-04 - mae: 0.0194 - val_loss: 0.0095 - val_mae: 0.0571\n",
       "Epoch 69/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 4.9876e-04 - mae: 0.0138epoch 68: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.4748e-04 - mae: 0.0109 - val_loss: 0.0092 - val_mae: 0.0508\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 9.4754e-04 - mae: 0.0199 - val_loss: 0.0097 - val_mae: 0.0599\n",
       "Epoch 70/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.2962e-04 - mae: 0.0089epoch 69: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.3536e-04 - mae: 0.0103 - val_loss: 0.0095 - val_mae: 0.0509\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 0.0011 - mae: 0.0227 - val_loss: 0.0092 - val_mae: 0.0524\n",
       "Epoch 71/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.0602e-04 - mae: 0.0105epoch 70: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.5277e-04 - mae: 0.0108 - val_loss: 0.0093 - val_mae: 0.0519\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 0.0031 - mae: 0.0364 - val_loss: 0.0107 - val_mae: 0.0592\n",
       "Epoch 72/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.6651e-04 - mae: 0.0094epoch 71: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.0162e-04 - mae: 0.0094 - val_loss: 0.0095 - val_mae: 0.0511\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 0.0025 - mae: 0.0342 - val_loss: 0.0098 - val_mae: 0.0525\n",
       "Epoch 73/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.0006e-04 - mae: 0.0077epoch 72: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.0275e-04 - mae: 0.0094 - val_loss: 0.0095 - val_mae: 0.0520\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 0.0011 - mae: 0.0229 - val_loss: 0.0085 - val_mae: 0.0479\n",
       "Epoch 74/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.9072e-04 - mae: 0.0101epoch 73: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.9079e-04 - mae: 0.0093 - val_loss: 0.0095 - val_mae: 0.0508\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 4.0215e-04 - mae: 0.0138 - val_loss: 0.0082 - val_mae: 0.0468\n",
       "Epoch 75/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.7304e-04 - mae: 0.0100epoch 74: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.8449e-04 - mae: 0.0090 - val_loss: 0.0094 - val_mae: 0.0512\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 2.8597e-04 - mae: 0.0118 - val_loss: 0.0083 - val_mae: 0.0476\n",
       "Epoch 76/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.6417e-04 - mae: 0.0090epoch 75: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.9934e-04 - mae: 0.0094 - val_loss: 0.0092 - val_mae: 0.0511\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 3.1199e-04 - mae: 0.0125 - val_loss: 0.0083 - val_mae: 0.0471\n",
       "Epoch 77/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.7640e-04 - mae: 0.0098epoch 76: loss: 0.000, val_loss:0.009 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.2352e-04 - mae: 0.0104 - val_loss: 0.0095 - val_mae: 0.0521\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.8727e-04 - mae: 0.0097 - val_loss: 0.0083 - val_mae: 0.0476\n",
       "Epoch 78/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.6004e-04 - mae: 0.0120epoch 77: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.9087e-04 - mae: 0.0094 - val_loss: 0.0094 - val_mae: 0.0507\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.9828e-04 - mae: 0.0100 - val_loss: 0.0081 - val_mae: 0.0457\n",
       "Epoch 79/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 5.1177e-04 - mae: 0.0109epoch 78: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.2326e-04 - mae: 0.0101 - val_loss: 0.0097 - val_mae: 0.0507\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.8352e-04 - mae: 0.0095 - val_loss: 0.0078 - val_mae: 0.0450\n",
       "Epoch 80/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.2068e-04 - mae: 0.0097epoch 79: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.4460e-04 - mae: 0.0105 - val_loss: 0.0096 - val_mae: 0.0519\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.4978e-04 - mae: 0.0084 - val_loss: 0.0080 - val_mae: 0.0456\n",
       "Epoch 81/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.5838e-04 - mae: 0.0099epoch 80: loss: 0.000, val_loss:0.010 val_mae:0.057\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.5869e-04 - mae: 0.0141 - val_loss: 0.0103 - val_mae: 0.0567\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.6882e-04 - mae: 0.0090 - val_loss: 0.0080 - val_mae: 0.0456\n",
       "Epoch 82/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 7.5091e-04 - mae: 0.0216epoch 81: loss: 0.001, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.4578e-04 - mae: 0.0164 - val_loss: 0.0096 - val_mae: 0.0516\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.4526e-04 - mae: 0.0083 - val_loss: 0.0079 - val_mae: 0.0455\n",
       "Epoch 83/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.6681e-04 - mae: 0.0142epoch 82: loss: 0.000, val_loss:0.010 val_mae:0.057\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.7241e-04 - mae: 0.0153 - val_loss: 0.0103 - val_mae: 0.0567\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.4768e-04 - mae: 0.0084 - val_loss: 0.0079 - val_mae: 0.0456\n",
       "Epoch 84/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 5.5546e-04 - mae: 0.0205epoch 83: loss: 0.001, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.3904e-04 - mae: 0.0163 - val_loss: 0.0095 - val_mae: 0.0515\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.4460e-04 - mae: 0.0083 - val_loss: 0.0079 - val_mae: 0.0451\n",
       "Epoch 85/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 9.0688e-04 - mae: 0.0184epoch 84: loss: 0.000, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.7274e-04 - mae: 0.0130 - val_loss: 0.0100 - val_mae: 0.0530\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.4741e-04 - mae: 0.0084 - val_loss: 0.0082 - val_mae: 0.0479\n",
       "Epoch 86/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.8415e-04 - mae: 0.0157epoch 85: loss: 0.000, val_loss:0.010 val_mae:0.052\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.5522e-04 - mae: 0.0153 - val_loss: 0.0100 - val_mae: 0.0515\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.5070e-04 - mae: 0.0086 - val_loss: 0.0080 - val_mae: 0.0457\n",
       "Epoch 87/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 6.5558e-04 - mae: 0.0139epoch 86: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.8282e-04 - mae: 0.0115 - val_loss: 0.0096 - val_mae: 0.0507\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.4048e-04 - mae: 0.0082 - val_loss: 0.0079 - val_mae: 0.0462\n",
       "Epoch 88/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.2386e-04 - mae: 0.0088epoch 87: loss: 0.000, val_loss:0.009 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 2.0087e-04 - mae: 0.0101 - val_loss: 0.0094 - val_mae: 0.0503\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.3027e-04 - mae: 0.0078 - val_loss: 0.0080 - val_mae: 0.0454\n",
       "Epoch 89/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 8.3486e-05 - mae: 0.0066epoch 88: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.7802e-04 - mae: 0.0093 - val_loss: 0.0098 - val_mae: 0.0511\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.2991e-04 - mae: 0.0080 - val_loss: 0.0078 - val_mae: 0.0455\n",
       "Epoch 90/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.2205e-04 - mae: 0.0083epoch 89: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.5105e-04 - mae: 0.0084 - val_loss: 0.0095 - val_mae: 0.0510\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.3704e-04 - mae: 0.0082 - val_loss: 0.0079 - val_mae: 0.0453\n",
       "Epoch 91/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.1719e-04 - mae: 0.0086epoch 90: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.8085e-04 - mae: 0.0094 - val_loss: 0.0093 - val_mae: 0.0512\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.2009e-04 - mae: 0.0075 - val_loss: 0.0079 - val_mae: 0.0455\n",
       "Epoch 92/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.2617e-04 - mae: 0.0096epoch 91: loss: 0.001, val_loss:0.009 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 5.4463e-04 - mae: 0.0156 - val_loss: 0.0094 - val_mae: 0.0528\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.1882e-04 - mae: 0.0075 - val_loss: 0.0078 - val_mae: 0.0458\n",
       "Epoch 93/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.4550e-04 - mae: 0.0141epoch 92: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.3336e-04 - mae: 0.0129 - val_loss: 0.0093 - val_mae: 0.0513\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.4176e-04 - mae: 0.0085 - val_loss: 0.0079 - val_mae: 0.0464\n",
       "Epoch 94/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.8600e-04 - mae: 0.0125epoch 93: loss: 0.000, val_loss:0.010 val_mae:0.053\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 4.9802e-04 - mae: 0.0161 - val_loss: 0.0097 - val_mae: 0.0528\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.3053e-04 - mae: 0.0080 - val_loss: 0.0079 - val_mae: 0.0464\n",
       "Epoch 95/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 4.8060e-04 - mae: 0.0145epoch 94: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 3.2077e-04 - mae: 0.0130 - val_loss: 0.0095 - val_mae: 0.0513\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.6369e-04 - mae: 0.0092 - val_loss: 0.0079 - val_mae: 0.0456\n",
       "Epoch 96/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 2.8333e-04 - mae: 0.0120epoch 95: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.8914e-04 - mae: 0.0096 - val_loss: 0.0095 - val_mae: 0.0511\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.5218e-04 - mae: 0.0089 - val_loss: 0.0079 - val_mae: 0.0464\n",
       "Epoch 97/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.2910e-04 - mae: 0.0085epoch 96: loss: 0.000, val_loss:0.009 val_mae:0.050\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.7548e-04 - mae: 0.0092 - val_loss: 0.0092 - val_mae: 0.0504\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.9467e-04 - mae: 0.0104 - val_loss: 0.0080 - val_mae: 0.0462\n",
       "Epoch 98/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 9.3943e-05 - mae: 0.0075epoch 97: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.6663e-04 - mae: 0.0092 - val_loss: 0.0094 - val_mae: 0.0514\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.2992e-04 - mae: 0.0081 - val_loss: 0.0080 - val_mae: 0.0459\n",
       "Epoch 99/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 3.0676e-04 - mae: 0.0112epoch 98: loss: 0.000, val_loss:0.010 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.7511e-04 - mae: 0.0093 - val_loss: 0.0095 - val_mae: 0.0506\n",
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.4517e-04 - mae: 0.0084 - val_loss: 0.0079 - val_mae: 0.0461\n",
       "Epoch 100/100\n",
-      " 1/29 [>.............................] - ETA: 0s - loss: 1.0778e-04 - mae: 0.0081epoch 99: loss: 0.000, val_loss:0.009 val_mae:0.051\n",
-      "29/29 [==============================] - 0s 2ms/step - loss: 1.5412e-04 - mae: 0.0089 - val_loss: 0.0091 - val_mae: 0.0507\n"
+      "29/29 [==============================] - 0s 2ms/step - loss: 1.2088e-04 - mae: 0.0076 - val_loss: 0.0079 - val_mae: 0.0456\n"
      ]
     }
    ],
    "source": [
-    "model.fit(md,val_fraction=0.1, val_key='refractive_index', lr=0.0005, epochs = 100, batch_size = 128, xscale='minmax',yscale=None, verbose=1)"
+    "model.fit(md,\n",
+    "          val_fraction=0.1,\n",
+    "          val_key='refractive_index',\n",
+    "          lr=0.0005,\n",
+    "          epochs = 100,\n",
+    "          batch_size = 128,\n",
+    "          xscale='minmax',\n",
+    "          yscale=None,\n",
+    "          verbose=1\n",
+    "         )"
    ]
   },
   {
@@ -1956,15 +1601,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:root:Saving model...\n",
-      "INFO:root:Saved model to out/MODNet_refractive_index(.json/.h5/.pkl)\n"
+      "2021-02-24 14:37:16,879 - modnet - INFO - Saving model...\n",
+      "2021-02-24 14:37:16,894 - modnet - INFO - Saved model to out/MODNet_refractive_index(.json/.h5/.pkl)\n"
      ]
     }
    ],
@@ -1984,9 +1629,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:modnet]",
+   "display_name": "Python (modnet-develop)",
    "language": "python",
-   "name": "conda-env-modnet-py"
+   "name": "modnet-develop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/modnet/featurizers/featurizers.py b/modnet/featurizers/featurizers.py
index b87ad858..cdbb1350 100644
--- a/modnet/featurizers/featurizers.py
+++ b/modnet/featurizers/featurizers.py
@@ -77,9 +77,18 @@ def featurize(self, df: pd.DataFrame) -> pd.DataFrame:
             The featurized DataFrame.
 
         """
-        df_composition = self.featurize_composition(df)
-        df_structure = self.featurize_structure(df)
-        df_site = self.featurize_site(df)
+        df_composition = pd.DataFrame([])
+        if self.composition_featurizers or self.oxid_composition_featurizers:
+            df_composition = self.featurize_composition(df)
+            
+        df_structure = pd.DataFrame([])            
+        if self.structure_featurizers:
+            df_structure = self.featurize_structure(df)
+            
+        df_site = pd.DataFrame([])
+        if self.site_featurizers:
+            df_site = self.featurize_site(df)           
+
         return df_composition.join(df_structure.join(df_site, lsuffix="l"), rsuffix="r")
 
     def _fit_apply_featurizers(
@@ -136,9 +145,6 @@ def featurize_composition(self, df: pd.DataFrame) -> pd.DataFrame:
 
         """
 
-        if not (self.composition_featurizers or self.oxid_composition_featurizers):
-            return pd.DataFrame([])
-
         df = df.copy()
 
         if self.composition_featurizers:
@@ -175,9 +181,6 @@ def featurize_structure(self, df: pd.DataFrame) -> pd.DataFrame:
 
         """
 
-        if not self.structure_featurizers:
-            return pd.DataFrame([])
-
         LOG.info("Applying structure featurizers...")
         df = df.copy()
         df = self._fit_apply_featurizers(df, self.structure_featurizers, "structure")
@@ -201,9 +204,6 @@ def featurize_site(self, df: pd.DataFrame, aliases: Optional[Dict[str, str]] = N
 
         """
 
-        if not self.site_featurizers:
-            return pd.DataFrame([])
-
         LOG.info("Applying site featurizers...")
 
         df = df.copy()
diff --git a/modnet/featurizers/presets/debreuck_2020.py b/modnet/featurizers/presets/debreuck_2020.py
index 3bf4ab20..2139df1b 100644
--- a/modnet/featurizers/presets/debreuck_2020.py
+++ b/modnet/featurizers/presets/debreuck_2020.py
@@ -142,6 +142,7 @@ def featurize_structure(self, df):
         renames some fields and cleans the output dataframe.
 
         """
+
         df = super().featurize_structure(df)
 
         dist = df["RadialDistributionFunction|radial distribution function"].iloc[0][
@@ -195,7 +196,6 @@ def featurize_site(self, df):
             "GeneralizedRadialDistributionFunction": "GeneralizedRDF",
             "AGNIFingerprints": "AGNIFingerPrint",
             "BondOrientationalParameter": "BondOrientationParameter",
-            "GaussianSymmFunc": "ChemEnvSiteFingerprint|GaussianSymmFunc",
         }
         df = super().featurize_site(df, aliases=aliases)
         df = df.loc[:, (df != 0).any(axis=0)]
@@ -205,5 +205,6 @@ def featurize_site(self, df):
 
 class CompositionOnlyFeaturizer(DeBreuck2020Featurizer):
     oxid_composition_featurizers = ()
-    structure_featurizes = ()
+    structure_featurizers = ()
     site_featurizers = ()
+
diff --git a/modnet/preprocessing.py b/modnet/preprocessing.py
index 2a109fa7..8466e82e 100644
--- a/modnet/preprocessing.py
+++ b/modnet/preprocessing.py
@@ -101,23 +101,26 @@ def nmi_target(df_feat: pd.DataFrame, df_target: pd.DataFrame,
     for x in mutual_info.index:
         mutual_info.loc[x, target_name] = mutual_info.loc[x, target_name] / ((target_mi + diag[x])/2)
 
+    mutual_info.fillna(0, inplace=True) # if na => no relation => set to zero
     return mutual_info
 
 
-def get_cross_nmi(df_feat: pd.DataFrame, **kwargs) -> pd.DataFrame:
+def get_cross_nmi(df_feat: pd.DataFrame, drop_thr: float = 0.2, return_entropy = False, **kwargs) -> pd.DataFrame:
     """
     Computes the Normalized Mutual Information (NMI) between input features.
 
     Args:
         df_feat (pandas.DataFrame): Dataframe containing the input features for
             which the NMI with the target variable is to be computed.
+        drop_thr: Features having an information entropy (or self mutual information) threshold below this value will be dropped.
+        return_entropy: If set to True, the information entropy of each feature is also returned
         **kwargs: Keyword arguments to be passed down to the
             :py:func:`mutual_info_regression` function from scikit-learn. This
             can be useful e.g. for testing purposes.
 
     Returns:
-        pd.DataFrame: pandas.DataFrame containing the Normalized Mutual Information between features.
-
+        mutual_info: pandas.DataFrame containing the Normalized Mutual Information between features.
+        if return_entropy=True : (mutual_info, diag): With diag a dictionary with all features as keys and information entropy as values.
     """
 
     if kwargs.get("random_state"):
@@ -138,9 +141,9 @@ def get_cross_nmi(df_feat: pd.DataFrame, **kwargs) -> pd.DataFrame:
             random_state=seed,
             **kwargs
         )[0]
-        if diag[x_feat] < 0.2 or abs(df_feat[x_feat].max() - df_feat[x_feat].min()) < EPS:
-            mutual_info.loc[x_feat, x_feat] = np.nan
-            diag[x_feat] = np.nan
+        if diag[x_feat] < drop_thr or abs(df_feat[x_feat].max() - df_feat[x_feat].min()) < EPS:
+            mutual_info.drop(x_feat, axis=0, inplace=True)
+            mutual_info.drop(x_feat, axis=1, inplace=True)
         else:
             mutual_info.loc[x_feat, x_feat] = 1.0
 
@@ -155,8 +158,12 @@ def get_cross_nmi(df_feat: pd.DataFrame, **kwargs) -> pd.DataFrame:
             )[0] / (0.5 * (diag[x_feat] + diag[y_feat]))
             mutual_info.loc[y_feat, x_feat] = mutual_info.loc[x_feat, y_feat] = I_xy
 
-    return mutual_info
+    mutual_info.fillna(0, inplace=True) # if na => no relation => set to zero
 
+    if return_entropy:
+        return mutual_info, diag # diag can be useful for future elimination based on entropy without the need of recomputing the cross NMI
+    else:
+        return mutual_info
 
 def get_rr_p_parameter_default(nn: int) -> float:
     """
@@ -414,20 +421,22 @@ class MODData:
         __modnet_version__ (str): The MODNet version number used to create the object
         cross_nmi (pd.DataFrame): If feature selection has been performed, this attribute
             stores the normalized mutual information between all features.
-        num_classes: Dictionary defining the target types (classification or regression).
+        feature_entropy (Dictionary): Information entropy of all features. Only computed after a call to compute cross_nmi.
+        num_classes (Dictionary): Defining the target types (classification or regression).
             Should be constructed as follows: key: string giving the target name; value: integer n,
             with n=0 for regression and n>=2 for classification with n the number of classes.
     """
 
     def __init__(
         self,
-        structures: Optional[List[Union[Structure, Composition]]] = None,
+        materials: Optional[List[Union[Structure, Composition]]] = None,
         targets: Optional[Union[List[float], np.ndarray]] = None,
         target_names: Optional[Iterable] = None,
         structure_ids: Optional[Iterable] = None,
         num_classes: Optional[Dict[str, int]] = None,
         df_featurized: Optional[pd.DataFrame] = None,
         featurizer: Optional[Union[MODFeaturizer, str]] = None,
+        structures: Optional[List[Union[Structure, Composition]]] = None,
     ):
         """ Initialise the MODData object either from a list of structures
         or from an already featurized dataframe. Prediction targets per
@@ -436,7 +445,7 @@ def __init__(
         structures.
 
         Args:
-            structures: list of structures to featurize and predict.
+            materials: list of structures or compositions to featurize and predict.
             targets: optional List of targets corresponding to each structure. When learning on multiple targets this
              is a ndarray where each column corresponds to a target, i.e. of shape (n_materials,n_targets).
             target_names: optional Iterable (e.g. list) of names of target properties to use in the dataframe.
@@ -448,6 +457,7 @@ def __init__(
                 featurizing a new one. Should be passed without structures.
             featurizer: optional MODFeaturizer object to use for featurization, or string
                 preset to look up in presets dictionary.
+            structures: deprecated (alias to materials for backward compatibility) do not use this.
 
         """
 
@@ -457,11 +467,14 @@ def __init__(
         self.featurizer = featurizer
         self.cross_nmi = None
 
-        if structures is not None and self.df_featurized is not None:
-            if len(structures) != len(self.df_featurized):
+        if structures is not None: # overwrite materials for backward compatibility
+            materials = structures
+
+        if materials is not None and self.df_featurized is not None:
+            if len(materials) != len(self.df_featurized):
                 raise RuntimeError("Mismatched shape of structures and passed df_featurized")
 
-        if structures is None and self.df_featurized is None:
+        if materials is None and self.df_featurized is None:
             raise RuntimeError(
                 "At least one of `structures` or `df_featurized` should be passed to `MODData`."
             )
@@ -469,12 +482,12 @@ def __init__(
         if targets is not None:
             targets = np.array(targets).reshape((len(targets), -1))
 
-        if structures is not None and targets is not None:
-            if np.shape(targets)[0] != len(structures):
-                raise ValueError(f"Targets must have same length as structures: {np.shape(targets)} vs {len(structures)}")
+        if materials is not None and targets is not None:
+            if np.shape(targets)[0] != len(materials):
+                raise ValueError(f"Targets must have same length as structures: {np.shape(targets)} vs {len(materials)}")
 
-        if structures is not None and isinstance(structures[0], Composition):
-            structures = [CompositionContainer(s) for s in structures]
+        if materials is not None and isinstance(materials[0], Composition):
+            materials = [CompositionContainer(s) for s in materials]
             self._composition_only = True
 
         if isinstance(featurizer, str):
@@ -505,11 +518,11 @@ def __init__(
             if len(set(structure_ids)) != len(structure_ids):
                 raise ValueError("List of IDs (`structure_ids`) provided must be unique.")
 
-            if len(structure_ids) != len(structures):
+            if len(structure_ids) != len(materials):
                 raise ValueError("List of IDs (`structure_ids`) must have same length as list of structure.")
 
         else:
-            num_entries = len(structures) if structures is not None else len(df_featurized)
+            num_entries = len(materials) if materials is not None else len(df_featurized)
             structure_ids = [f"id{i}" for i in range(num_entries)]
 
         if targets is not None:
@@ -521,7 +534,7 @@ def __init__(
                 self.num_classes.update(num_classes)
 
         # set up dataframe for structures with columns (id, structure)
-        self.df_structure = pd.DataFrame({'id': structure_ids, 'structure': structures})
+        self.df_structure = pd.DataFrame({'id': structure_ids, 'structure': materials})
         self.df_structure.set_index('id', inplace=True)
 
     def featurize(self, fast: bool = False, db_file: str = 'feature_database.pkl', n_jobs=None):
@@ -635,7 +648,10 @@ def feature_selection(
 
         if self.cross_nmi is None:
             df = self.df_featurized.copy()
-            self.cross_nmi = get_cross_nmi(df)
+            self.cross_nmi, self.feature_entropy = get_cross_nmi(df, return_entropy = True)
+
+        if self.cross_nmi.isna().sum().sum() > 0:
+            raise RuntimeError("Cross NMI (`moddata.cross_nmi`) contains NaN values, consider setting them to zero.")
 
         for i, name in enumerate(self.names):
             LOG.info(f"Starting target {i+1}/{len(self.names)}: {self.names[i]} ...")
@@ -670,6 +686,11 @@ def structures(self) -> List[Union[Structure, CompositionContainer]]:
         """Returns the list of `pymatgen.Structure` objects. """
         return list(self.df_structure["structure"])
 
+    @property
+    def compositions(self) -> List[Union[Structure, CompositionContainer]]:
+        """Returns the list of materials as`pymatgen.Composition` objects. """
+        return [s.composition for s in self.df_structure["structure"]]
+
     @property
     def targets(self) -> np.ndarray:
         """ Returns a ndarray of prediction targets. """
diff --git a/modnet/tests/conftest.py b/modnet/tests/conftest.py
index 4ce26a7b..7460d577 100644
--- a/modnet/tests/conftest.py
+++ b/modnet/tests/conftest.py
@@ -10,8 +10,12 @@
         "1786091a73f53985b08868c5be431a3c700f7f1776002df28ebf3a12a79ab1a1"
     ),
     "MP_2018.6_small.zip": (
-        "937a29dad32d18e47c84eb7c735ed8af09caede21d2339c379032fbd40c463d8"
-        "ca377d9e3a777710b5741295765f6c12fbd7ab56f9176cc0ca11c9120283d878"
+        "b7f31d066113d1ad1f4f3990250019835bad96c18eddefd4a0b3866fd23a6037"
+        "d1ad90b1f4a1e08d12d7f0a7ce2ebcf4a1a4b673500e1118543b687dbd1749e6"
+    ),
+    "MP_2018.6_small_composition.zip": (
+        "59f8c4e546df005799e3fb7a1e64daa0edfece48fa346ab0d2efe92aa107d0d1"
+        "b14bb16f56bfe3f54e5a9020d088a268536f6ad86134e264ed7547b4fd583c79"
     ),
 }
 
@@ -49,6 +53,14 @@ def small_moddata():
     """
     return _load_moddata("MP_2018.6_small.zip")
 
+@pytest.fixture(scope="function")
+def small_moddata_composition():
+    """Loads the small 5-structure featurized subset of MP.2018.6 composition only for use
+    in other tests, checking only the hash.
+
+    """
+    return _load_moddata("MP_2018.6_small_composition.zip")
+
 
 @pytest.fixture(scope="module")
 def tf_session():
diff --git a/modnet/tests/data/MP_2018.6_small.zip b/modnet/tests/data/MP_2018.6_small.zip
index ab8533bc6be9f7e10c9c28bedab17359e5ebe0be..22624e3c227a3bf2253e0bb832671beb26f778f8 100644
GIT binary patch
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diff --git a/modnet/tests/test_preprocessing.py b/modnet/tests/test_preprocessing.py
index 73edefbd..170d6b7b 100644
--- a/modnet/tests/test_preprocessing.py
+++ b/modnet/tests/test_preprocessing.py
@@ -213,10 +213,10 @@ def test_get_cross_nmi():
     df_feat = pd.DataFrame({'x': x, 'y': y, 'z': z, 'c': c})
 
     df_cross_nmi = get_cross_nmi(df_feat=df_feat, n_neighbors=2)
-    assert df_cross_nmi.shape == (4, 4)
-    expected = np.ones((4, 4))
-    expected[3, :] = expected[:, 3] = np.nan
-    expected[3, 3] = np.nan
+    assert df_cross_nmi.shape == (3, 3)
+    expected = np.ones((3, 3))
+    #expected[3, :] = expected[:, 3] = 0
+    #expected[3, 3] = 0
     np.testing.assert_allclose(
         np.array(df_cross_nmi, dtype=np.float64),
         expected,
@@ -281,7 +281,6 @@ def test_small_moddata_featurization(small_moddata):
     old_cols = sorted(old.df_featurized.columns.tolist())
 
     for i in range(len(old_cols)):
-        print(new_cols[i], old_cols[i])
         assert new_cols[i] == old_cols[i]
 
     np.testing.assert_array_equal(
@@ -295,6 +294,28 @@ def test_small_moddata_featurization(small_moddata):
             old.df_featurized[col].to_numpy(),
         )
 
+def test_small_moddata_composition_featurization(small_moddata_composition):
+    """ This test creates a new MODData from the MP 2018.6 structures. """
+
+    reference = small_moddata_composition
+    compositions = reference.compositions
+
+    new = MODData(materials=compositions)
+    new.featurize(fast=False, n_jobs=1)
+
+    new_cols = sorted(new.df_featurized.columns.tolist())
+    ref_cols = sorted(reference.df_featurized.columns.tolist())
+
+    for i in range(len(ref_cols)):
+        # print(new_cols[i], ref_cols[i])
+        assert new_cols[i] == ref_cols[i]
+
+    for col in new.df_featurized.columns:
+        np.testing.assert_almost_equal(
+            new.df_featurized[col].to_numpy(),
+            reference.df_featurized[col].to_numpy(),
+        )
+
 def test_small_moddata_feature_selection_classif(small_moddata):
     """ This test creates classifier MODData and test the feature selection method """
 
@@ -399,7 +420,7 @@ def test_moddata_splits(subset_moddata):
 def test_precomputed_cross_nmi(small_moddata):
 
     new = MODData(
-        structures=small_moddata.structures,
+        materials=small_moddata.structures,
         targets=small_moddata.targets,
         target_names=small_moddata.names,
         df_featurized=small_moddata.df_featurized,
diff --git a/pretrained/vib_thermo.h5 b/pretrained/vib_thermo.h5
index 6902c22a421e786c879e9b812eb14e940a61c404..be270b287e8153e1fd333fc3c49083bc588e1253 100644
GIT binary patch
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diff --git a/pretrained/vib_thermo.json b/pretrained/vib_thermo.json
index 01c13d39..ccfc7f48 100644
--- a/pretrained/vib_thermo.json
+++ b/pretrained/vib_thermo.json
@@ -1 +1 @@
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\ No newline at end of file
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