TY - JOUR
T1 - DP-GEN
T2 - A concurrent learning platform for the generation of reliable deep learning based potential energy models
AU - Zhang, Yuzhi
AU - Wang, Haidi
AU - Chen, Weijie
AU - Zeng, Jinzhe
AU - Zhang, Linfeng
AU - Wang, Han
AU - E, Weinan
N1 - Funding Information:
The authors thank Marcos F. Calegari Andrade, Hsin-Yu Ko, Jianxing Huang, Yunpei Liu, Mengchao Shi, Fengbo Yuan, and Yongbin Zhuang for helps and discussions. We are grateful for computing time provided by the TIGRESS High Performance Computer Center at Princeton University, the High-performance Computing Platform of Peking University, and the Beijing Institute of Big Data Research. The work of L. Z. and W. E was supported in part by a gift from iFlytek to Princeton University, USA, the ONR, USA grant N00014-13-1-0338, and the Center Chemistry in Solution and at Interfaces (CSI) funded by the DOE, USA Award DE-SC001934. The work of Han Wang is supported by the National Science Foundation of China under Grant No. 11871110, the National Key Research and Development Program of China under Grant Nos. 2016YFB0201200 and 2016YFB0201203, and Beijing Academy of Artificial Intelligence (BAAI), China. The work of J. Z. is partially supported by National Innovation and Entrepreneurship Training Program for Undergraduate, China (201910269080).
Funding Information:
The authors thank Marcos F. Calegari Andrade, Hsin-Yu Ko, Jianxing Huang, Yunpei Liu, Mengchao Shi, Fengbo Yuan, and Yongbin Zhuang for helps and discussions. We are grateful for computing time provided by the TIGRESS High Performance Computer Center at Princeton University, the High-performance Computing Platform of Peking University, and the Beijing Institute of Big Data Research. The work of L. Z. and W. E was supported in part by a gift from iFlytek to Princeton University, USA , the ONR, USA grant N00014-13-1-0338 , and the Center Chemistry in Solution and at Interfaces (CSI) funded by the DOE, USA Award DE-SC001934 . The work of Han Wang is supported by the National Science Foundation of China under Grant No. 11871110 , the National Key Research and Development Program of China under Grant Nos. 2016YFB0201200 and 2016YFB0201203 , and Beijing Academy of Artificial Intelligence (BAAI), China . The work of J. Z. is partially supported by National Innovation and Entrepreneurship Training Program for Undergraduate, China ( 201910269080 ).
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/8
Y1 - 2020/8
N2 - In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed ”on-the-fly” learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN. Program summary: Program Title: DP-GEN Program Files doi: http://dx.doi.org/10.17632/sxybkgc5xc.1 Licensing provisions: LGPL Programming language: Python Nature of problem: Generating reliable deep learning based potential energy models with minimal human intervention and computational cost. Solution method: The concurrent learning scheme is implemented. Supports for sampling configuration space with LAMMPS, generating ab initio data with Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit are provided. Supports for different machines including workstations, high performance clusters and cloud machines are provided. Supports for job management tools including Slurm, PBS, LSF are provided.
AB - In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed ”on-the-fly” learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN. Program summary: Program Title: DP-GEN Program Files doi: http://dx.doi.org/10.17632/sxybkgc5xc.1 Licensing provisions: LGPL Programming language: Python Nature of problem: Generating reliable deep learning based potential energy models with minimal human intervention and computational cost. Solution method: The concurrent learning scheme is implemented. Supports for sampling configuration space with LAMMPS, generating ab initio data with Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit are provided. Supports for different machines including workstations, high performance clusters and cloud machines are provided. Supports for job management tools including Slurm, PBS, LSF are provided.
KW - Concurrent learning
KW - Deep learning
KW - Many-body potential energy
UR - http://www.scopus.com/inward/record.url?scp=85080099356&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85080099356&partnerID=8YFLogxK
U2 - 10.1016/j.cpc.2020.107206
DO - 10.1016/j.cpc.2020.107206
M3 - Article
AN - SCOPUS:85080099356
SN - 0010-4655
VL - 253
JO - Computer Physics Communications
JF - Computer Physics Communications
M1 - 107206
ER -