DeePMD-kit v2: A software package for deep potential models

DOI DOI PDF 研究データあり 被引用文献6件 参考文献8件 オープンアクセス
  • Jinzhe Zeng
    Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University 1 , Piscataway, New Jersey 08854, USA
  • Duo Zhang
    AI for Science Institute 2 , Beijing 100080, People’s Republic of China
  • Denghui Lu
    HEDPS, CAPT, College of Engineering, Peking University 5 , Beijing 100871, People’s Republic of China
  • Pinghui Mo
    College of Electrical and Information Engineering, Hunan University 6 , Changsha, People’s Republic of China
  • Zeyu Li
    Yuanpei College, Peking University 7 , Beijing 100871, People’s Republic of China
  • Yixiao Chen
    Program in Applied and Computational Mathematics, Princeton University 8 , Princeton, New Jersey 08540, USA
  • Marián Rynik
    Department of Experimental Physics, Comenius University 9 , Mlynská Dolina F2, 842 48 Bratislava, Slovakia
  • Li’ang Huang
    Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University 10 , Beijing 100084, People’s Republic of China
  • Ziyao Li
    Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University 1 , Piscataway, New Jersey 08854, USA
  • Shaochen Shi
    ByteDance Research, Zhonghang Plaza 12 , No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
  • Yingze Wang
    Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University 1 , Piscataway, New Jersey 08854, USA
  • Haotian Ye
    Yuanpei College, Peking University 7 , Beijing 100871, People’s Republic of China
  • Ping Tuo
    AI for Science Institute 2 , Beijing 100080, People’s Republic of China
  • Jiabin Yang
    Baidu, Inc. 14 , Beijing, People’s Republic of China
  • Ye Ding
    Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University 1 , Piscataway, New Jersey 08854, USA
  • Yifan Li
    Department of Chemistry, Princeton University 17 , Princeton, New Jersey 08544, USA
  • Davide Tisi
    Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University 1 , Piscataway, New Jersey 08854, USA
  • Qiyu Zeng
    Department of Physics, National University of Defense Technology 20 , Changsha, Hunan 410073, People’s Republic of China
  • Han Bao
    AI for Science Institute 2 , Beijing 100080, People’s Republic of China
  • Yu Xia
    ByteDance Research, Zhonghang Plaza 12 , No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
  • Jiameng Huang
    AI for Science Institute 2 , Beijing 100080, People’s Republic of China
  • Koki Muraoka
    Department of Chemical System Engineering, The University of Tokyo 24 , 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
  • Yibo Wang
    DP Technology 3 , Beijing 100080, People’s Republic of China
  • Junhan Chang
    Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University 1 , Piscataway, New Jersey 08854, USA
  • Fengbo Yuan
    DP Technology 3 , Beijing 100080, People’s Republic of China
  • Sigbjørn Løland Bore
    Hylleraas Centre for Quantum Molecular Sciences and Department of Chemistry, University of Oslo 25 , P.O. Box 1033 Blindern, 0315 Oslo, Norway
  • Chun Cai
    AI for Science Institute 2 , Beijing 100080, People’s Republic of China
  • Yinnian Lin
    Wangxuan Institute of Computer Technology, Peking University 26 , Beijing 100871, People’s Republic of China
  • Bo Wang
    Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University 27 , Shanghai 200062, People’s Republic of China
  • Jiayan Xu
    School of Chemistry and Chemical Engineering, Queen’s University Belfast 28 , Belfast BT9 5AG, United Kingdom
  • Jia-Xin Zhu
    State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University 29 , Xiamen 361005, People’s Republic of China
  • Chenxing Luo
    Department of Applied Physics and Applied Mathematics, Columbia University 30 , New York, New York 10027, USA
  • Yuzhi Zhang
    DP Technology 3 , Beijing 100080, People’s Republic of China
  • Rhys E. A. Goodall
    Independent Researcher 31 , London, United Kingdom
  • Wenshuo Liang
    DP Technology 3 , Beijing 100080, People’s Republic of China
  • Anurag Kumar Singh
    Department of Data Science, Indian Institute of Technology 32 , Palakkad, Kerala, India
  • Sikai Yao
    DP Technology 3 , Beijing 100080, People’s Republic of China
  • Jingchao Zhang
    NVIDIA AI Technology Center (NVAITC) 33 , Santa Clara, California 95051, USA
  • Renata Wentzcovitch
    DP Technology 3 , Beijing 100080, People’s Republic of China
  • Jiequn Han
    Center for Computational Mathematics, Flatiron Institute 35 , New York, New York 10010, USA
  • Jie Liu
    College of Electrical and Information Engineering, Hunan University 6 , Changsha, People’s Republic of China
  • Weile Jia
    AI for Science Institute 2 , Beijing 100080, People’s Republic of China
  • Darrin M. York
    Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University 1 , Piscataway, New Jersey 08854, USA
  • Weinan E
    AI for Science Institute 2 , Beijing 100080, People’s Republic of China
  • Roberto Car
    Department of Chemistry, Princeton University 17 , Princeton, New Jersey 08544, USA
  • Linfeng Zhang
    AI for Science Institute 2 , Beijing 100080, People’s Republic of China
  • Han Wang
    DP Technology 3 , Beijing 100080, People’s Republic of China

書誌事項

公開日
2023-08-01
権利情報
  • https://creativecommons.org/licenses/by/4.0/
  • https://creativecommons.org/licenses/by/4.0/
DOI
  • 10.1063/5.0155600
  • 10.48550/arxiv.2304.09409
公開者
AIP Publishing

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説明

<jats:p>DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.</jats:p>

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