Since models do not know the periodic table, the nature of the element types in a given structure needs to be encoded numerically. Often, as in the case of RACs, this is done using element properties such as the atomic number or electronegativity. However, it is well known that some encodings such as atomic numbers lose the clustering of elements according to their periodic properties, which can be an important inductive bias for a machine learning model. Therefore, mofdscribe allows users to flexibly choose from a wide variety of elemental properties in addition to other encodings such as the (modified) Pettifor scales that have been shown to better capture similarities of elements across the periodic table. For instance, Pettifor scales can be Listing 2 | Example of using aggregations in mofdscribe. Many featurizers compute more than one feature vector per structure; for instance, one feature vector per atom. In this case, the data must be further processed to construct one fixed-size feature vector per structure. To ensure that the resulting feature vectors are permutation invariant (that is, do not depend on the arbitrary numbering of atoms in the structure), one typically uses aggregation functions such as the average, sum, maximum, or minimum. At every point where aggregations are computed, we allow users to customize the ones used. Users can simply specify the desired ones as a tuple of strings; for example, ("median", "range", "geom_av") would aggregate the features using the median, range, and geometric average. racs_featurizer = RACS( prop_agg=("avg", "product", "diff", "sum"), corr_agg=("avg", "sum", "range"), bb_agg=("avg", "sum", "range"), ) thought of as the "optimal one-dimensional periodic table": similar elements are neighboring in this representation (in the original scale with the goal to achieve an optimal map of the stability of AB compounds). The impact the choice of encoding can have is shown in Figure , where we find improvements of over 50 % by using the modified Pettifor scale for encoding elements in revised autocorrelation functions (in contrast to using the electronegativity). Also here, mofdscribe exposes these encoding options on all featurizers where they apply. This makes this approximation visible and allows users to tune the encodings to increase the predictive performance or interpretability of their models.