A Machine Learning Model and Database for The Identification of New Metal-Insulator Transition Compounds

ORAL

Abstract

One of the main bottlenecks in the discovery of new thermally-driven metal-insulator transition (MIT) materials is the lack of a clear database of MIT compounds, and their relevant features. We have built a database of MIT and stoichiometrically related materials, and trained a machine learning model designed to classify whether a material is an MIT material or not [1]. Our easily interpretable model allows us to identify new features, such as the Average Deviation of the Covalent Radius and its interplay with the Range Mendeleev Number, as well as others. We also built an online pipeline where one can upload their own structures, and obtain a prediction on whether the material is a metal, an insulator, or an MIT material and tested it on previously identified materials [2].

[1] https://arxiv.org/abs/2010.13306
[2] https://arxiv.org/abs/2004.07365

*This work was supported in part by the National Science Foundation (NSF) under award number DMR-1729303. The information, data, or work presented herein was also funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001209. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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Presenters

  • Alexandru Bogdan Georgescu

    • McCormick School of Engineering, Department of Materials Science and Engineering, Northwestern University
    • Simons Foundation
    • Center for Computational Quantum Physics, Flatiron Institute
    • McCormick School of Engineering, Department of Materials Science & Engineering, Northwestern University

Authors

  • Alexandru Bogdan Georgescu

    • McCormick School of Engineering, Department of Materials Science and Engineering, Northwestern University
    • Simons Foundation
    • Center for Computational Quantum Physics, Flatiron Institute
    • McCormick School of Engineering, Department of Materials Science & Engineering, Northwestern University

  • Peiwen Ren

    • McCormick School of Engineering, Department of Materials Science and Engineering, Northwestern University

  • Aubrey Toland

    • Department of Materials Science and Engineering, Massachusetts Institute of Technology

  • Nicholas Wagner

    • McCormick School of Engineering, Department of Materials Science and Engineering, Northwestern University

  • Elsa Olivetti

    • Department of Materials Science and Engineering, Massachusetts Institute of Technology

  • James M Rondinelli

    • Northwestern University
    • McCormick School of Engineering, Department of Materials Science and Engineering, Northwestern University
    • Department of Materials Science and Engineering, Northwestern University