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Condensed Matter > Materials Science
Title: Machine-Learning Based Selection and Synthesis of Candidate Metal-Insulator Transition Metal Oxides
(Submitted on 21 Mar 2024)
Abstract: 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.
Submission history
From: Alexandru Bogdan Georgescu [view email][v1] Thu, 21 Mar 2024 00:55:15 GMT (3296kb,D)
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