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Metal-insulator transition (MIT) compounds are materials that may exhibitinsulating or metallic behavior, depending on the physical conditions, and areof immense fundamental interest owing to their potential applications inemerging microelectronics. There is a dearth of thermally-driven MIT materials,however, which makes delineating these compounds from those that areexclusively insulating or metallic challenging. Here we report a materialdatabase comprising temperature-controlled MITs (and metals and insulators withsimilar chemical composition and stoichiometries to the MIT compounds) fromhigh quality experimental literature, built through a combination ofmaterials-domain knowledge and natural language processing. We featurize thedataset using compositional, structural, and energetic descriptors, includingtwo MIT relevant energy scales, an estimated Hubbard interaction and the chargetransfer energy, as well as the structure-bond-stress metric referred to as theglobal-instability index (GII). We then perform supervised classification,constructing three electronic-state classifiers: metal vs non-metal (M),insulator vs non-insulator (I), and MIT vs non-MIT (T). We identify twoimportant descriptors that separate metals, insulators, and MIT materials in a2D feature space: the average deviation of the covalent radius and the range ofthe Mendeleev number. We further elaborate on other important features (GII andEwald energy), and examine how they affect classification of binary vanadiumand titanium oxides. We discuss the relationship of these atomic features tothe physical interactions underlying MITs in the rare-earth nickelate family.Last, we implement an online version of the classifiers, enabling quickprobabilistic class predictions by uploading a crystallographic structure file.

Database, Features, and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds