Machine learning of accurate energy-conserving molecular force fields
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- Stefan Chmiela
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany.
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- Alexandre Tkatchenko
- Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg.
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- Huziel E. Sauceda
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany.
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- Igor Poltavsky
- Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg.
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- Kristof T. Schütt
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany.
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- Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany.
書誌事項
- 公開日
- 2017-05-05
- DOI
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- 10.1126/sciadv.1603015
- 10.14279/depositonce-6849
- 10.48550/arxiv.1611.04678
- 公開者
- American Association for the Advancement of Science (AAAS)
説明
<jats:p>The law of energy conservation is used to develop an efficient machine learning approach to construct accurate force fields.</jats:p>
収録刊行物
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- Science Advances
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Science Advances 3 (5), e1603015-, 2017-05-05
American Association for the Advancement of Science (AAAS)
関連研究データ
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キーワード
- Chemical Physics (physics.chem-ph)
- energy conservation
- path integrals
- force field
- FOS: Physical sciences
- potential-energy surface
- molecular dynamics
- 500 Naturwissenschaften und Mathematik
- machine learning
- Physics - Chemical Physics
- kernel regression
- : Mathematics [G03] [Physical, chemical, mathematical & earth Sciences]
- : Mathématiques [G03] [Physique, chimie, mathématiques & sciences de la terre]
- gradient field
- Research Articles