A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Zuo, Yunxing; Chen, Chi; Li, Xiangguo; Deng, Zhi; Chen, Yiming; Behler, Jörg; Csányi, Gábor; Shapeev, Alexander V; Thompson, Aidan P; Wood, Mitchell A; Ong, Shyue Ping

doi:10.1021/acs.jpca.9b08723

Title: A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Journal Article · 08 January 2020 · Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory

DOI: https://doi.org/10.1021/acs.jpca.9b08723 · OSTI ID:1559244

Zuo, Yunxing ^[1]; Chen, Chi ^[1]; Li, Xiangguo ^[1]; Deng, Zhi ^[1]; Chen, Yiming ^[1]; Behler, Jörg ^[2]; Csányi, Gábor ^[3]; Shapeev, Alexander V ^[4]; Thompson, Aidan P ^[5]; Wood, Mitchell A ^[5]; Ong, Shyue Ping ^[1]

Univ. of California, San Diego, CA (United States). Dept. of NanoEngineering
Univ. of Goettingen (Germany). Inst. of Physical and Theoretical Chemistry
Univ. of Cambridge (United Kingdom). Dept. of Engineering
Skolkovo Institute of Science and Technology, Moscow (Russian Federation)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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 ML-IAPs based on four local environment descriptors --- Behler-Parrinello symmetry functions, 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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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231; AC04-94AL85000

OSTI ID:: 1559244

Report Number(s):: SAND2019-7998J; ark:/13030/qt0j64t2hz

Journal Information:: Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory, Journal Name: Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory Journal Issue: 4 Vol. 124; ISSN 1089-5639

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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