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Title: Accurate force field for molybdenum by machine learning large materials data

Abstract

In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods still do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including energies, forces, stresses, elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large-scale, long-time scale simulations.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. Univ. of California, San Diego, CA (United States). Dept. of NanoEngineering
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1559139
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review Materials
Additional Journal Information:
Journal Volume: 1; Journal Issue: 4; Journal ID: ISSN 2475-9953
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Chen, Chi, Deng, Zhi, Tran, Richard, Tang, Hanmei, Chu, Iek-Heng, and Ong, Shyue Ping. Accurate force field for molybdenum by machine learning large materials data. United States: N. p., 2017. Web. doi:10.1103/physrevmaterials.1.043603.
Chen, Chi, Deng, Zhi, Tran, Richard, Tang, Hanmei, Chu, Iek-Heng, & Ong, Shyue Ping. Accurate force field for molybdenum by machine learning large materials data. United States. doi:10.1103/physrevmaterials.1.043603.
Chen, Chi, Deng, Zhi, Tran, Richard, Tang, Hanmei, Chu, Iek-Heng, and Ong, Shyue Ping. Fri . "Accurate force field for molybdenum by machine learning large materials data". United States. doi:10.1103/physrevmaterials.1.043603. https://www.osti.gov/servlets/purl/1559139.
@article{osti_1559139,
title = {Accurate force field for molybdenum by machine learning large materials data},
author = {Chen, Chi and Deng, Zhi and Tran, Richard and Tang, Hanmei and Chu, Iek-Heng and Ong, Shyue Ping},
abstractNote = {In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods still do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including energies, forces, stresses, elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large-scale, long-time scale simulations.},
doi = {10.1103/physrevmaterials.1.043603},
journal = {Physical Review Materials},
number = 4,
volume = 1,
place = {United States},
year = {2017},
month = {9}
}

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