期刊
JOURNAL OF PHYSICAL CHEMISTRY A
卷 124, 期 4, 页码 731-745出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpca.9b08723
关键词
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资金
- Office of Naval Research (ONR) Young Investigator Program (YIP) [N00014-16-1-2621]
- National Science Foundation [ACI-1053575]
- Deutsche Forschungsgemeinschaft [Be3264/11-2, 329898176]
- Russian Science Foundation [18-13-00479]
- U.S. Department of Energy's National Nuclear Security Administration [DE-NA0003525]
- Russian Science Foundation [18-13-00479] Funding Source: Russian Science Foundation
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors-using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.
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