Accelerating Discovery of Metal-Insulator Transition Compounds Using Physics-Informed Machine Learning

TL;DR

This paper introduces a physics-informed machine learning framework that significantly speeds up the discovery and analysis of metal-insulator transition materials, combining classification, DFT calculations, and synthesis prediction.

Contribution

It presents a novel integrated machine learning approach that accelerates the identification, understanding, and synthesis of MIT materials, reducing reliance on costly simulations and experiments.

Findings

Successfully identified promising MIT candidates from a crystal database.

Estimated transition temperatures using machine learning models.

Provided insights into microscopic mechanisms of MIT in selected compounds.

Abstract

Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the complexity of underlying mechanisms, and the challenges of experimental validation. Here, we present a physics-informed machine learning framework that accelerates the discovery of thermally driven MIT materials. Using a trained classifier, we screen a crystal structure database to identify promising candidates for higher fidelity simulations. We focus on Ca$_2$Fe$_3$O$_8$, CaCo$_2$O$_4$, and CaMn$_2$O$_4$, and use density functional theory (DFT) to determine their electronic and magnetic ground states and assess their microscopic MIT mechanisms. We further apply machine learning regression models to estimate their transition temperatures and employ synthesis prediction tools to identify likely precursors and reaction routes. This integrated approach reduces the time and effort required to identify, understand, and synthesize new MIT materials, providing a generalizable pathway for accelerating correlated quantum materials discovery.