Having datasets and standard implementations of featurization algorithms is not all that is needed to make machine learning for reticular materials routine and comparable. To reach standard Fig. | Adding chemical resolution to geometric descriptors. Descriptors, such as the ones derived from topological data analysis, operate on the full structure (top row) and capture the geometry and connectivity of a material described by its atomic coordinates. In mofdscribe, we allow users to also incorporate information from different atom types (i.e, not treat all atom types the same way). Users can customize the extent to which they want to lift this many-to-one mapping by adding channels for different atom types. By default, for instance, the descriptors from topological data analysis are computed for all atoms, the metallic substructure, and the organic substructureall yielding different topological signatures as evident from the persistent images. In this plot, we use the vectorization of persistent diagrams using Gaussian mixture models with and without chemical information (here, we consider the C-H-N-O, halogen, and metal substructures). For this analysis, we optimize the full pipeline (including pre-processing and the model) using automated machine learning. The plots visualize effect sizes in terms of Hedges' g. The points indicate the coefficient of determination (r 2 on a holdout test set, shown on the left axes) of the models trained to predict the methane deliverable capacity (DC), Henry coefficient (K H ), as well as the CO 2 Henry coefficient and low pressure (l.p.) uptake. The blue points are for the model trained with chemistry (c.) information; the green ones indicate the coefficients of determination of the models trained without chemistry information (n.c.). To quantify the effect, we bootstrap the Hedges' g (a suitable effect size metric in the case of little data, shown on the right axes) and show it with a kernel density estimate. In all cases, the addition of chemistry shows very large effects. practices for digital reticular chemistry, we also need to address model validation and comparison.