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

Georgescu, Alexandru B.; Ren, Peiwen; Toland, Aubrey R; Zhang, Shengtong; Miller, Kyle D.; Apley, Daniel W.; Olivetti, Elsa A.; Wagner, Nicholas; Rondinelli, James M.

doi:10.1021/acs.chemmater.1c00905

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

Author(s): Alexandru B. Georgescu ¹ , Peiwen Ren ¹ , Aubrey R. Toland ² , Shengtong Zhang ³ , Kyle D. Miller ¹ , Daniel W. Apley ³ , Elsa A. Olivetti ² , Nicholas Wagner ¹ , James M. Rondinelli ¹

Publication date (Electronic): July 06 2021

Journal: Chemistry of Materials

Publisher: American Chemical Society (ACS)

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Elsa A. Olivetti: (View ORCID Profile)

James M. Rondinelli: (View ORCID Profile)

Journal

Title: Chemistry of Materials

Abbreviated Title: Chem. Mater.

Publisher: American Chemical Society (ACS)

ISSN (Print): 0897-4756

ISSN (Electronic): 1520-5002

Publication date Created: July 27 2021

Publication date (Electronic): July 06 2021

Publication date (Print): July 27 2021

Volume: 33

Issue: 14

Pages: 5591-5605

Affiliations

[1 ]Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States

[2 ]Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

[3 ]Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston 60208, Illinois United States

Article

DOI: 10.1021/acs.chemmater.1c00905

SO-VID: ad29248f-421d-43b6-88c4-a62becd55537

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https://creativecommons.org/licenses/by-nc-nd/4.0/

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

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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

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