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Title: Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals

Abstract

In recent years, efficient interatomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, for translation, rotation, permutation of homonuclear atoms, among others. In this work, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over the well-established high-performing embedded atom method (EAM) and modified EAM potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of a Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties, such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. Lastly, this paper provides a systematic model development process for multicomponent alloy systems, including an efficientmore » procedure to optimize the hyperparameters in the model fitting, and paves the way for long-time large-scale simulations of such systems.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. Univ. of California San Diego, La Jolla, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1544214
Grant/Contract Number:  
[N00014-16-1-2621; N00014-16-12569; ACI-1053575]
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review B
Additional Journal Information:
[ Journal Volume: 98; Journal Issue: 9]; Journal ID: ISSN 2469-9950
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY

Citation Formats

Li, Xiang-Guo, Hu, Chongze, Chen, Chi, Deng, Zhi, Luo, Jian, and Ong, Shyue Ping. Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals. United States: N. p., 2018. Web. doi:10.1103/PhysRevB.98.094104.
Li, Xiang-Guo, Hu, Chongze, Chen, Chi, Deng, Zhi, Luo, Jian, & Ong, Shyue Ping. Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals. United States. doi:10.1103/PhysRevB.98.094104.
Li, Xiang-Guo, Hu, Chongze, Chen, Chi, Deng, Zhi, Luo, Jian, and Ong, Shyue Ping. Tue . "Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals". United States. doi:10.1103/PhysRevB.98.094104. https://www.osti.gov/servlets/purl/1544214.
@article{osti_1544214,
title = {Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals},
author = {Li, Xiang-Guo and Hu, Chongze and Chen, Chi and Deng, Zhi and Luo, Jian and Ong, Shyue Ping},
abstractNote = {In recent years, efficient interatomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, for translation, rotation, permutation of homonuclear atoms, among others. In this work, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over the well-established high-performing embedded atom method (EAM) and modified EAM potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of a Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties, such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. Lastly, this paper provides a systematic model development process for multicomponent alloy systems, including an efficient procedure to optimize the hyperparameters in the model fitting, and paves the way for long-time large-scale simulations of such systems.},
doi = {10.1103/PhysRevB.98.094104},
journal = {Physical Review B},
number = [9],
volume = [98],
place = {United States},
year = {2018},
month = {9}
}

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