Active learning of uniformly accurate interatomic potentials for materials simulation
Abstract
An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.
- Authors:
-
- Princeton Univ., NJ (United States). Program in Applied and Computational Mathematics
- Inst. of Applied Physics and Computational Mathematics (IAPCM), Beijing (China); CAEP Software Center for High Performance Numerical Simulation, Beijing (China)
- Inst. of Applied Physics and Computational Mathematics (IAPCM), Beijing (China). Lab. of Computational Physics
- Princeton Univ., NJ (United States). Dept. of Chemistry, Dept. of Physics, Program in Applied and Computational Mathematics, Princeton Inst. for the Science and Technology of Materials
- Princeton Univ., NJ (United States). Dept. of Mathematics and program in Applied and Computational Mathematics
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1542459
- Alternate Identifier(s):
- OSTI ID: 1496508
- Grant/Contract Number:
- SC0019394
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review Materials
- Additional Journal Information:
- Journal Volume: 3; Journal Issue: 2; Journal ID: ISSN 2475-9953
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Zhang, Linfeng, Lin, De-Ye, Wang, Han, Car, Roberto, and E, Weinan. Active learning of uniformly accurate interatomic potentials for materials simulation. United States: N. p., 2019.
Web. doi:10.1103/PhysRevMaterials.3.023804.
Zhang, Linfeng, Lin, De-Ye, Wang, Han, Car, Roberto, & E, Weinan. Active learning of uniformly accurate interatomic potentials for materials simulation. United States. https://doi.org/10.1103/PhysRevMaterials.3.023804
Zhang, Linfeng, Lin, De-Ye, Wang, Han, Car, Roberto, and E, Weinan. Mon .
"Active learning of uniformly accurate interatomic potentials for materials simulation". United States. https://doi.org/10.1103/PhysRevMaterials.3.023804. https://www.osti.gov/servlets/purl/1542459.
@article{osti_1542459,
title = {Active learning of uniformly accurate interatomic potentials for materials simulation},
author = {Zhang, Linfeng and Lin, De-Ye and Wang, Han and Car, Roberto and E, Weinan},
abstractNote = {An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.},
doi = {10.1103/PhysRevMaterials.3.023804},
journal = {Physical Review Materials},
number = 2,
volume = 3,
place = {United States},
year = {Mon Feb 25 00:00:00 EST 2019},
month = {Mon Feb 25 00:00:00 EST 2019}
}
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