Listing 1 | Complete featurization usage example. The featurizers in mofdscribe can be easily combined with the ones implemented in matminer. All featurizers also share the same utility methods, such as citation and feature_labels, and can be computed for multiple structures using featurize_many. The MOFMultipleFeaturizer additionally provides the option to compute the primitive cell for each structure before featurization. )) ] features = featurizer.featurize(s) labels = featurizer.feature_labels() citations = featurizer.citations() reticular materials, this approximation is widely used as part of the feature set (which is often supplemented with global features, see below) via revised autocorrelation functions (RACs), smooth overlap of atomic positions (SOAP) fingerprints, local geometry descriptors, or the average mean distance descriptor by Widdowson et al. For all those atom-centered descriptors, one can compute N descriptors for a structure with N atoms. Since different materials will have different numbers of atoms N in their unit cells, one typically needs to perform an aggregation operation, such as computing the arithmetic mean of all the atom-centered feature vectors, to construct a fixed-length descriptor that is permutation invariant. The latter is important since we do not want our descriptors to change when we change the (arbitrary) numbering of atoms (that is only an artifact of digitally encoding materials). Of course, there is not one ideal choice for the aggregation operation. One might benefit from additional expressivity by using multiple aggregations, for instance, the standard deviation alongside the arithmetic mean or other Pythagorean means or robust measures (e.g., trimean, mean absolute deviation). Therefore, in our library, the user can-where applicable-simply provide all aggregation combinations of interest (see List- ) can lead to large (>20 %) improvements in predictive performance on material property prediction tasks. By exposing all these options, mofdscribe makes these approximations visible to the users and allows users to tune them for better predictive performance.