Machine-Learning Based Selection and Synthesis of Candidate Metal-Insulator Transition Metal Oxides

• Published in: arXiv.org
• Main Authors: Georgescu, Alexandru B, Ren, Peiwen, Karpovich, Christopher, Olivetti, Elsa, Rondinelli, James M
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 21.03.2024
• Subjects: Chemical synthesis; Crystal structure; Density functional theory; Electronic structure; Insulators; Machine learning; Metal-insulator transition; Optoelectronic devices; Transition metal oxides

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.