  The discovery of materials that exhibit a metal-insulator transition (MIT) is
key to the development of multiple types of novel efficient microelectronic and
optoelectronic devices. However, identifying MIT materials is challenging due
to a combination of high computational cost of electronic structure
calculations needed to understand their mechanism, the mechanisms' complexity,
and the labor-intensive experimental validation process. To that end, we use a
machine learning classification model to rapidly screen a high-throughput
crystal structure database to identify candidate compounds exhibiting
thermally-driven MITs. We focus on three candidate oxides, Ca$_2$Fe$_3$O$_8$,
CaCo$_2$O$_4$, and CaMn$_2$O$_4$, and identify their MIT mechanism using
high-fidelity density functional theory calculations. Then, we provide a
probabilistic estimate of which synthesis reactions may lead to their
realization. Our approach couples physics-informed machine learning, density
functional theory calculations, and machine learning-suggested synthesis to
reduce the time to discovery and synthesis of new technologically relevant
materials.
