Journal ID (nlm-ta): Chem Rev
Journal ID (iso-abbrev): Chem Rev
Journal ID (publisher-id): cr
Journal ID (coden): chreay
Title:
Chemical Reviews
Publisher:
American Chemical Society
ISSN
(Print):
0009-2665
ISSN
(Electronic):
1520-6890
Publication date
(Electronic):
07
July
2021
Publication date
(Print):
25
August
2021
Volume: 121
Issue: 16
,
Machine Learning at the Atomic Scale
Pages: 9816-9872
Affiliations
[†
]Department
of Chemical and Petroleum Engineering Swanson School of Engineering, University of Pittsburgh , Pittsburgh, Pennsylvania 15261, United States
[‡
]Department
of Physics and Materials Science, University
of Luxembourg , L-1511 Luxembourg City, Luxembourg
[¶
]Accelerate
Programme for Scientific Discovery , Department
of Computer Science and Technology, 15 J. J. Thomson Avenue, Cambridge CB3 0FD, United Kingdom
Cavendish Laboratory, University of Cambridge , J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom
[§
]Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin , 10587, Berlin, Germany
[∥
]Machine
Learning Group, Technische Universität
Berlin , 10587, Berlin, Germany
[▽
]Department
of Artificial Intelligence, Korea University , Anam-dong, Seongbuk-gu, Seoul, 02841, Korea;
[⬡
]Max-Planck-Institut für Informatik , 66123 Saarbrücken, Germany
[⬢
]Google Research , Brain Team, 10117 Berlin, Germany
Author notes
Author information
Article
DOI: 10.1021/acs.chemrev.1c00107
PMC ID: 8391798
PubMed ID: 34232033
SO-VID: 8f4ef374-fa8d-46a3-ace5-42ea4cff459c
Copyright © © 2021 The Authors. Published
by American
Chemical Society
License:
Permits non-commercial access and re-use, provided that author attribution and integrity
are maintained; but does not permit creation of adaptations or other derivative works
(
https://creativecommons.org/licenses/by-nc-nd/4.0/).
Funded by: Institute for Information and Communications Technology Promotion, doi 10.13039/501100010418;
Award ID: 2017-0-00451
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Funded by: Division of Chemical, Bioengineering, Environmental, and Transport Systems, doi 10.13039/100000146;
Award ID: 1705592