Integrating Physics-based Models with Data-driven Methods for Materials Discovery
Metal-insulator transition (MIT) compounds are materials that can undergo an electronic phase changes and are promising platforms to build next-generation low-power microelectronics. Accelerated discovery is challenging using high-throughput screening because high-fidelity quantum-mechanical simulations are computationally prohibitive to perform. We solved this problem by building a supervised machine-learning model that can classify whether a material, given its structure as input, would exhibit a thermal MIT.
- •We created the first the public dataset of thermally-driven MIT compounds using natural language processing schemes in collaboration with the Olivetti group (at MIT) and publicly disseminated the new database.
- •Compounds were featurized using new physics-based descriptors, which were integrated into the matminer Python library for data mining the properties of materials.
- •We also deployed an easy-to-use online pipeline that can enable quick probabilistic of any crystalline material. Promising compounds identified with classification model are undergoing experimental validation, in collaboration with the Wilson group at UCSB.
Related Project: Structure Genome of Metal-Insulator Transitions