Started descriptor text

docs/source/advanced_usage/descriptors.rst

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 Tuning descriptors
 ==================
 
+The data conversion shown in the basic MALA guide is straightforward from
+an interface point of view, however it is non-trivial to determine the
+correct hyperparameters for the bispectrum descriptors. Ideally, cutoff radius
+and dimensionality of the descriptors should be chosen such that atomic
+environments around each point in space are accurately represented.
+
+Some physical intuition factors into this process, as detailed in
+the MALA publication on `hyperparameter optimization <https://doi.org/10.1088/2632-2153/ac9956>`_.
+There, it is discussed that too large cutoff radii lead to descriptors
+that are too similar for different points in space, complicating the
+ML training, while too small radii lead to descriptors not containing
+enough physical information, and do not yield physically accurate models.
+Likewise, the dimensionality of the expansion of the atomic density
+needs to match the amount of information one wants to encode.
+
+In the publication mentioned above, a method has been devised based on these
+principles, the so called average cosine similarity distance (ACSD) analysis.
+Based on cosine similarities between descriptor and target vectors for
+distinct points, optimal descriptor hyperparameters can be determined; for
+more detail, please refer to the publication.
+
+Within MALA, this analysis is available as a special hyperparameter
+optimization routine, as showcased in the example ``advanced/ex03
+