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

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 scien...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AIP Publishing LLC 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445636/
https://www.ncbi.nlm.nih.gov/pubmed/37526163
http://dx.doi.org/10.1063/5.0155600
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author 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
author_facet 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
author_sort Zeng, Jinzhe
collection PubMed
description 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.
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spelling pubmed-104456362023-08-24 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 J Chem Phys ARTICLES 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. AIP Publishing LLC 2023-08-07 2023-08-01 /pmc/articles/PMC10445636/ /pubmed/37526163 http://dx.doi.org/10.1063/5.0155600 Text en © 2023 Author(s). https://creativecommons.org/licenses/by/4.0/All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle ARTICLES
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
DeePMD-kit v2: A software package for deep potential models
title DeePMD-kit v2: A software package for deep potential models
title_full DeePMD-kit v2: A software package for deep potential models
title_fullStr DeePMD-kit v2: A software package for deep potential models
title_full_unstemmed DeePMD-kit v2: A software package for deep potential models
title_short DeePMD-kit v2: A software package for deep potential models
title_sort deepmd-kit v2: a software package for deep potential models
topic ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445636/
https://www.ncbi.nlm.nih.gov/pubmed/37526163
http://dx.doi.org/10.1063/5.0155600
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