In machine learning for chemistry and materials, the most commonly used assumption is the locality approximation. In practice, this assumes that a property does not depend on the entire crystal but that the main contributions are from the local environment (which can be justified based on the principle of "nearsightness of electronic matter" 52 ). For example, in our model for the oxidation state of the metal in a MOF, the features are computed for the metal and the atoms of the linkers surrounding the metal. By reducing the learning problem to local environments, the locality approximation allows a model trained on small fragments to generalize to large structures (which are harder to sample as there are combinatorially more of them). Note that mofdscribe is interoperable with matminer, wherefore featurizers implemented in matminer can be used with those implemented in mofdscribe. For a full overview of implemented featurizers, see Extended Data Table .