Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions-Reference-Cited by-同舟云学术

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

Published:2018-06-28 Issue:24 Volume:148 Page:241725
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:The Journal of Chemical Physics

Author:

Nguyen Thuong T.¹²,Székely Eszter³,Imbalzano Giulio⁴,Behler Jörg⁵^ORCID,Csányi Gábor³,Ceriotti Michele⁴^ORCID,Götz Andreas W.²,Paesani Francesco¹²^ORCID

Affiliation:

1. Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, USA

2. San Diego Supercomputer Center, University of California, San Diego, La Jolla, California 92093, USA

3. Engineering Department, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom

4. Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

5. Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstr. 6, 37077 Göttingen, Germany

Funder

National Science Foundation

Deutsche Forschungsgemeinschaft

H2020 European Research Council

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/1.5024577

Reference90 articles.

1. Equation of State Calculations by Fast Computing Machines

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3. Studies in Molecular Dynamics. I. General Method

4. Studies in Molecular Dynamics. II. Behavior of a Small Number of Elastic Spheres

5. Water as an Active Constituent in Cell Biology

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