One common approach is to provide explanations of the model predictions using feature importance analysis. Translating the outcomes of these analyses into useful design rules depends upon the ability to relate ML features to tangible, physical properties; no amount of post-hoc explanation allows one to engineer new materials if the features themselves cannot be exploited. In our case, by employing MOF strings that directly correspond to chemical structures, we ensure our features are not only physically meaningful but also potentially actionable: an experienced chemist, equipped with the appropriate experimental tools, could replicate the linker modifications suggested by the model. Furthermore, we can (in principle)