A key design aspect for featurizers is the length scale they operate on (Figure ). In mofdscribe, we distinguish featurizers operating on the local, atom-centered neighborhoods, the building units (BU), and the full, global structure. Depending on the learning tasks, different scales will be more relevant. For instance, for gas separations, we need to describe the textural properties of the pore (global) along with the chemistry of the building unit (BU/atom-centered). Therefore, it is important that featurizers operating on different scales can easily be combined. In mofdscribe, all featurizers can be used in the same way and combined as needed, thereby enabling rapid experimentation. To make this possible, mofdscribe uses the featurizer design pattern popularized by the matminer package (see Listing 1 in which we compute a feature vector for a MOF by combining featurizers from all scopes). This design pattern, which bears similarities to the sklearn API, ensures consistency across how different featurizers are used and, in this way, enables composability and also makes them accessible to non-experts. By building on top of the matminer building blocks, mofdscribe is also fully interoperable with the featurizers implemented in the matminer library.