Understanding, discovery, and synthesis of 2D materials enabled by machine learning-Reference-Cited by-同舟云学术

Understanding, discovery, and synthesis of 2D materials enabled by machine learning

Published:2022 Issue:6 Volume:51 Page:1899-1925
ISSN:0306-0012
Container-title:Chemical Society Reviews
language:en
Short-container-title:Chem. Soc. Rev.

Author:

Ryu Byunghoon¹,Wang Luqing²³,Pu Haihui¹⁴,Chan Maria K. Y.²⁵,Chen Junhong¹⁴^ORCID

Affiliation:

1. Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, Illinois 60439, USA

2. Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA

3. The Materials Research Center, Northwestern University, Evanston, Illinois 60208, USA

4. Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA

5. Northwestern Argonne Institute of Science and Engineering, Evanston, Illinois 60208, USA

Abstract

Machine learning (ML) is becoming an effective tool for studying 2D materials.

Funder

Argonne National Laboratory

National Science Foundation

Office of Science

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CS/D1CS00503K

Reference50 articles.

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4. Machine learning-driven new material discovery

5. Recent advances and applications of machine learning in solid-state materials science

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