Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds-Reference-Cited by-同舟云学术

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

Published:2021-07-06 Issue:14 Volume:33 Page:5591-5605
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Georgescu Alexandru B.¹,Ren Peiwen¹,Toland Aubrey R.²,Zhang Shengtong³,Miller Kyle D.¹,Apley Daniel W.³,Olivetti Elsa A.²^ORCID,Wagner Nicholas¹,Rondinelli James M.¹^ORCID

Affiliation:

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

Funder

Advanced Research Projects Agency - Energy

Division of Materials Research

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.1c00905

Reference72 articles.

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2. Machine Learning Many-Body Localization: Search for the Elusive Nonergodic Metal

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5. A steep-slope transistor based on abrupt electronic phase transition

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