Machine Learning Force Fields-Reference-Cited by-同舟云学术

Machine Learning Force Fields

Published:2021-03-11 Issue:16 Volume:121 Page:10142-10186
ISSN:0009-2665
Container-title:Chemical Reviews
language:en
Short-container-title:Chem. Rev.

Author:

Unke Oliver T.¹²^ORCID,Chmiela Stefan¹,Sauceda Huziel E.¹³^ORCID,Gastegger Michael¹²³,Poltavsky Igor⁴^ORCID,Schütt Kristof T.¹^ORCID,Tkatchenko Alexandre⁴^ORCID,Müller Klaus-Robert¹⁵⁶⁷⁸^ORCID

Affiliation:

1. Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany

2. DFG Cluster of Excellence “Unifying Systems in Catalysis” (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany

3. BASLEARN, BASF-TU Joint Lab, Technische Universität Berlin, 10587 Berlin, Germany

4. Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg

5. BIFOLD−Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

6. Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea

7. Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany

8. Google Research, Brain Team, Berlin, Germany

Funder

Bundesministerium f?r Bildung und Forschung

Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung

Deutsche Forschungsgemeinschaft

Institute for Information and Communications Technology Promotion

H2020 European Research Council

Korea University

Publisher

American Chemical Society (ACS)

Subject

General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.0c01111

Reference327 articles.

1. Dynamics of folded proteins

2. Scaling of Multimillion-Atom Biological Molecular Dynamics Simulation on a Petascale Supercomputer

3. Anton, a special-purpose machine for molecular dynamics simulation

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