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DeePMD-kit v2: A software package for deep potential models.
Zeng, Jinzhe; Zhang, Duo; Lu, Denghui; Mo, Pinghui; Li, Zeyu; Chen, Yixiao; Rynik, Marián; Huang, Li'ang; Li, Ziyao; Shi, Shaochen; Wang, Yingze; Ye, Haotian; Tuo, Ping; Yang, Jiabin; Ding, Ye; Li, Yifan; Tisi, Davide; Zeng, Qiyu; Bao, Han; Xia, Yu; Huang, Jiameng; Muraoka, Koki; Wang, Yibo; Chang, Junhan; Yuan, Fengbo; Bore, Sigbjørn Løland; Cai, Chun; Lin, Yinnian; Wang, Bo; Xu, Jiayan; Zhu, Jia-Xin; Luo, Chenxing; Zhang, Yuzhi; Goodall, Rhys E A; Liang, Wenshuo; Singh, Anurag Kumar; Yao, Sikai; Zhang, Jingchao; Wentzcovitch, Renata; Han, Jiequn; Liu, Jie; Jia, Weile; York, Darrin M; E, Weinan; Car, Roberto; Zhang, Linfeng; Wang, Han.
Afiliação
  • Zeng J; Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.
  • Zhang D; AI for Science Institute, Beijing 100080, People's Republic of China.
  • Lu D; DP Technology, Beijing 100080, People's Republic of China.
  • Mo P; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, People's Republic of China.
  • Li Z; HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People's Republic of China.
  • Chen Y; College of Electrical and Information Engineering, Hunan University, Changsha, People's Republic of China.
  • Rynik M; Yuanpei College, Peking University, Beijing 100871, People's Republic of China.
  • Huang L; Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08540, USA.
  • Li Z; Department of Experimental Physics, Comenius University, Mlynská Dolina F2, 842 48 Bratislava, Slovakia.
  • Shi S; Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, People's Republic of China.
  • Wang Y; Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.
  • Ye H; DP Technology, Beijing 100080, People's Republic of China.
  • Tuo P; ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People's Republic of China.
  • Yang J; Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.
  • Ding Y; DP Technology, Beijing 100080, People's Republic of China.
  • Li Y; Yuanpei College, Peking University, Beijing 100871, People's Republic of China.
  • Tisi D; AI for Science Institute, Beijing 100080, People's Republic of China.
  • Zeng Q; Baidu, Inc., Beijing, People's Republic of China.
  • Bao H; Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.
  • Xia Y; Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA.
  • Huang J; Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.
  • Muraoka K; Department of Physics, National University of Defense Technology, Changsha, Hunan 410073, People's Republic of China.
  • Wang Y; AI for Science Institute, Beijing 100080, People's Republic of China.
  • Chang J; ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People's Republic of China.
  • Yuan F; AI for Science Institute, Beijing 100080, People's Republic of China.
  • Bore SL; DP Technology, Beijing 100080, People's Republic of China.
  • Cai C; Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
  • Lin Y; DP Technology, Beijing 100080, People's Republic of China.
  • Wang B; Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.
  • Xu J; DP Technology, Beijing 100080, People's Republic of China.
  • Zhu JX; DP Technology, Beijing 100080, People's Republic of China.
  • Luo C; Hylleraas Centre for Quantum Molecular Sciences and Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, 0315 Oslo, Norway.
  • Zhang Y; AI for Science Institute, Beijing 100080, People's Republic of China.
  • Goodall REA; DP Technology, Beijing 100080, People's Republic of China.
  • Liang W; Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, People's Republic of China.
  • Singh AK; 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, Shanghai 200062, People's Republic of China.
  • Yao S; School of Chemistry and Chemical Engineering, Queen's University Belfast, Belfast BT9 5AG, United Kingdom.
  • Zhang J; State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China.
  • Wentzcovitch R; Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA.
  • Han J; DP Technology, Beijing 100080, People's Republic of China.
  • Liu J; Independent Researcher, London, United Kingdom.
  • Jia W; DP Technology, Beijing 100080, People's Republic of China.
  • York DM; Department of Data Science, Indian Institute of Technology, Palakkad, Kerala, India.
  • E W; DP Technology, Beijing 100080, People's Republic of China.
  • Car R; NVIDIA AI Technology Center (NVAITC), Santa Clara, California 95051, USA.
  • Zhang L; DP Technology, Beijing 100080, People's Republic of China.
  • Wang H; Center for Computational Mathematics, Flatiron Institute, New York, New York 10010, USA.
J Chem Phys ; 159(5)2023 Aug 07.
Article em En | MEDLINE | ID: mdl-37526163
ABSTRACT
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.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos