Average e0

README.md

@@ -75,7 +75,7 @@ To give a specific validation set, use the argument `--valid_file`. To set a lar
 
 To control the model's size, you need to change `--hidden_irreps`. For most applications, the recommended default model size is `--hidden_irreps='256x0e'` (meaning 256 invariant messages) or `--hidden_irreps='128x0e + 128x1o'`. If the model is not accurate enough, you can include higher order features, e.g., `128x0e + 128x1o + 128x2e`, or increase the number of channels to `256`. 
 
-It is usually preferred to add the isolated atoms to the training set, rather than reading in their energies through the command line like in the example above. To label them in the training set, set `config_type=IsolatedAtom` in their info fields. If you prefer not to use or do not know the energies of the isolated atoms, you should set `--model=ScaleShiftMACE` and pass a dictionary of 0's, e.g., `--E0s='{1:0.0, 6:0.0}'`. 
+It is usually preferred to add the isolated atoms to the training set, rather than reading in their energies through the command line like in the example above. To label them in the training set, set `config_type=IsolatedAtom` in their info fields. If you prefer not to use or do not know the energies of the isolated atoms, you can use the option `--E0s="average"` which estimates the atomic energies using least squares regression. 
 
 If the keyword `--swa` is enabled, the energy weight of the loss is increased for the last ~20% of the training epochs (from `--start_swa` epochs). This setting usually helps lower the energy errors. 
 

mace/data/__init__.py

@@ -3,6 +3,7 @@
 from .utils import (
     Configuration,
     Configurations,
+    compute_average_E0s,
     config_from_atoms,
     config_from_atoms_list,
     load_from_xyz,
@@ -20,4 +21,5 @@
     "config_from_atoms",
     "config_from_atoms_list",
     "AtomicData",
+    "compute_average_E0s",
 ]

mace/data/utils.py

@@ -6,6 +6,8 @@
 import ase.io
 import numpy as np
 
+from mace.tools import AtomicNumberTable
+
 Vector = np.ndarray  # [3,]
 Positions = np.ndarray  # [..., 3]
 Forces = np.ndarray  # [..., 3]
@@ -163,3 +165,33 @@ def load_from_xyz(
         forces_key=forces_key,
     )
     return atomic_energies_dict, configs
+
+
+def compute_average_E0s(
+    collections_train: Configuration, z_table: AtomicNumberTable
+) -> dict:
+    """
+    Function to compute the average interaction energy of each chemical element
+    returns dictionary of E0s
+    """
+    len_train = len(collections_train)
+    len_zs = len(z_table)
+    A = np.zeros((len_train, len_zs))
+    B = np.zeros(len_train)
+    for i in range(len_train):
+        B[i] = collections_train[i].energy
+        for j, z in enumerate(z_table.zs):
+            A[i, j] = np.count_nonzero(collections_train[i].atomic_numbers == z)
+    try:
+        E0s = np.linalg.lstsq(A, B, rcond=None)[0]
+        atomic_energies_dict = {}
+        for i, z in enumerate(z_table.zs):
+            atomic_energies_dict[z] = E0s[i]
+    except np.linalg.LinAlgError:
+        logging.warning(
+            "Failed to compute E0s using least squares regression, using the same for all atoms"
+        )
+        atomic_energies_dict = {}
+        for i, z in enumerate(z_table.zs):
+            atomic_energies_dict[z] = 0.0
+    return atomic_energies_dict

mace/tools/train.py

@@ -43,6 +43,7 @@ def train(
 ):
     lowest_loss = np.inf
     patience_counter = 0
+    swa_start = True
 
     if max_grad_norm is not None:
         logging.info(f"Using gradient clipping with tolerance={max_grad_norm:.3f}")
@@ -112,6 +113,9 @@ def train(
         if swa is None or epoch < swa.start:
             lr_scheduler.step(valid_loss)  # Can break if exponential LR, TODO fix that!
         else:
+            if swa_start:
+                logging.info("Changing loss based on SWA")
+                swa_start = False
             loss_fn = swa.loss_fn
             swa.model.update_parameters(model)
             swa.scheduler.step()

scripts/run_train.py

@@ -76,7 +76,7 @@ def get_dataset_from_xyz(
         # create list of tuples (config_type, list(Atoms))
         test_configs = data.test_config_types(all_test_configs)
         logging.info(
-            f"Loaded {len(all_test_configs)} test configurations from '{train_path}'"
+            f"Loaded {len(all_test_configs)} test configurations from '{test_path}'"
         )
     return (
         SubsetCollection(train=train_configs, valid=valid_configs, tests=test_configs),
@@ -107,12 +107,21 @@ def main() -> None:
     device = tools.init_device(args.device)
     tools.set_default_dtype(args.default_dtype)
 
+    try:
+        config_type_weights = ast.literal_eval(args.config_type_weights)
+        assert isinstance(config_type_weights, dict)
+    except Exception as e:  # pylint: disable=W0703
+        logging.warning(
+            f"Config type weights not specified correctly ({e}), using Default"
+        )
+        config_type_weights = {"Default": 1.0}
+
     # Data preparation
     collections, atomic_energies_dict = get_dataset_from_xyz(
         train_path=args.train_file,
         valid_path=args.valid_file,
         valid_fraction=args.valid_fraction,
-        config_type_weights=ast.literal_eval(args.config_type_weights),
+        config_type_weights=config_type_weights,
         test_path=args.test_file,
         seed=args.seed,
         energy_key=args.energy_key,
@@ -139,7 +148,21 @@ def main() -> None:
             logging.info(
                 "Atomic Energies not in training file, using command line argument E0s"
             )
-            atomic_energies_dict = ast.literal_eval(args.E0s)
+            if args.E0s.lower() == "average":
+                logging.info(
+                    "Computing average Atomic Energies using least squares regression"
+                )
+                atomic_energies_dict = data.compute_average_E0s(
+                    collections.train, z_table
+                )
+            else:
+                try:
+                    atomic_energies_dict = ast.literal_eval(args.E0s)
+                    assert isinstance(atomic_energies_dict, dict)
+                except Exception as e:
+                    raise RuntimeError(
+                        f"E0s specified invalidly, error {e} occured"
+                    ) from e
         else:
             raise RuntimeError(
                 "E0s not found in training file and not specified in command line"