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

TL;DR

This paper develops a machine learning framework using a curated database and physical descriptors to classify and understand thermally-driven metal-insulator transition compounds, aiding discovery and analysis.

Contribution

It introduces a comprehensive database and novel classifiers for MIT materials, integrating physical descriptors and enabling online predictions for the first time.

Findings

Identified key descriptors like covalent radius deviation and Mendeleev number range for classification.

Achieved high accuracy in distinguishing metals, insulators, and MIT materials.

Demonstrated the physical relevance of features through analysis of vanadium and titanium oxides.

Abstract

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Here we report a material database comprising temperature-controlled MITs (and metals and insulators with similar chemical composition and stoichiometries to the MIT compounds) from high quality experimental literature, built through a combination of materials-domain knowledge and natural language processing. We featurize the dataset using compositional, structural, and energetic descriptors, including two MIT relevant energy scales, an estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-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 two important descriptors that separate metals, insulators, and MIT materials in a 2D feature space: the average deviation of the covalent radius and the range of the Mendeleev number. We further elaborate on other important features (GII and Ewald energy), and examine how they affect classification of binary vanadium and titanium oxides. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. Last, we implement an online version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file.