Topological data analysis captures the geometry of chemical structures and materials, but these systems also have rich chemical information, as they are composed of different atoms. Thus, it is important to incorporate this chemical information into the representation-otherwise, materials with the same connectivity but different elements (for instance, Mg-MOF-74 vs. Ni-MOF-74) would be treated the same way, and a model would predict the same properties. To account for chemical information in a highly flexible way, which therefore can adjust to the amount of data available, mofdscribe allows decomposing the structures into structures that contain only certain elements (Figure ). By default, for instance, mofdscribe will perform the persistent homology analysis on the full structure, the metal substructure, the organic substructure, and the halogen substructure. However, users can customize the substructures that mofdscribe considers and tailor the featurization to the task at hand. As Figure shows, the inclusion of chemical information consistently increases predictive performance on MOF property prediction tasks (in our test cases by up to 20 %). We use the same approach to make the average minimum distance fingerprint pro-posed by Widdowson et al. chemistry-aware (and observe similar improvements on benchmark tasks there). Additionally, we also allow users to encode chemistry using so-called weighted alpha shapes. In this case, in addition to the coordinates, the (atomic) radii of different elements are used for persistence diagram construction to distinguish between different atom types.