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深度势能模型工具包v2:用于深度势能模型的软件包。

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

作者信息

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

机构信息

Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.

AI for Science Institute, Beijing 100080, People's Republic of China.

出版信息

J Chem Phys. 2023 Aug 7;159(5). doi: 10.1063/5.0155600.

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.

摘要

深度势能工具包(DeePMD-kit)是一个强大的开源软件包,它利用被称为深度势能(DP)模型的机器学习势能来促进分子动力学模拟。该软件包于2017年发布,已在物理、化学、生物学和材料科学等领域广泛用于研究原子系统。深度势能工具包的当前版本提供了许多高级功能,如深度势能-自编码器(DeepPot-SE)、基于注意力和混合描述符、拟合拉伸特性的能力、类型嵌入、模型偏差、DP范围校正、DP长程、对定制算子的图形处理单元支持、模型压缩、非冯·诺依曼分子动力学以及改进的可用性,包括文档、编译二进制包、图形用户界面和应用程序编程接口。本文概述了深度势能工具包软件包的当前主要版本,突出了其功能和技术细节。此外,本文还介绍了作为代表性应用进行分子动力学模拟的全面过程,对不同模型的准确性和效率进行了基准测试,并讨论了正在进行的开发情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a05d/10445636/9f1dbbb5d4fd/JCPSA6-000159-054801_1-g001.jpg

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