Journal of Physical Chemistry A, volume 124, issue 4, pages 731-745

Performance and Cost Assessment of Machine Learning Interatomic Potentials

Zuo Yunxing ¹

Chen Chi-Hua ¹

Li Xiang Guo ¹

Deng Zhi ¹

Chen Yiming ¹

Behler Jörg ²

Csányi G. ³

Shapeev Alexander V. ⁴

Thompson Aidan ⁵

Wood Mitchell A. ⁵

Ong Shyue Ping ¹

Hide authors affiliations Show authors affiliations: 5 affiliations

Department of NanoEngineering, University of California San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, California 92093-0448, United States |

Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Tammannstraße 6, 37077 Göttingen, Germany |

Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, U.K. |

⁴

Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Building 3, Moscow, 143026, Russia |

⁵

Center for Computing Research, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States |

Publication type: Journal Article

Publication date: 2020-01-09

American Chemical Society (ACS)

Journal: Journal of Physical Chemistry A

Quartile SCImago

Quartile WOS

Impact factor: 2.9

ISSN: 10895639, 15205215

DOI: 10.1021/acs.jpca.9b08723

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Physical and Theoretical Chemistry

Abstract

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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Citations by journals

	5 10 15 20 25 30 35 40
Journal of Chemical Physics	Journal of Chemical Physics, 40, 9.62% Journal of Chemical Physics 40 publications, 9.62%
npj Computational Materials	npj Computational Materials, 28, 6.73% npj Computational Materials 28 publications, 6.73%
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Physical Review B	Physical Review B, 19, 4.57% Physical Review B 19 publications, 4.57%
Physical Chemistry Chemical Physics	Physical Chemistry Chemical Physics, 15, 3.61% Physical Chemistry Chemical Physics 15 publications, 3.61%
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Journal of Physical Chemistry C	Journal of Physical Chemistry C, 11, 2.64% Journal of Physical Chemistry C 11 publications, 2.64%
Journal of Physical Chemistry Letters	Journal of Physical Chemistry Letters, 8, 1.92% Journal of Physical Chemistry Letters 8 publications, 1.92%
Journal of Applied Physics	Journal of Applied Physics, 7, 1.68% Journal of Applied Physics 7 publications, 1.68%
Journal of Physical Chemistry A	Journal of Physical Chemistry A, 7, 1.68% Journal of Physical Chemistry A 7 publications, 1.68%
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Chemistry of Materials	Chemistry of Materials, 5, 1.2% Chemistry of Materials 5 publications, 1.2%
Nature Communications	Nature Communications, 5, 1.2% Nature Communications 5 publications, 1.2%
Computer Physics Communications	Computer Physics Communications, 5, 1.2% Computer Physics Communications 5 publications, 1.2%
Chemical Reviews	Chemical Reviews, 5, 1.2% Chemical Reviews 5 publications, 1.2%
Journal of Physics Condensed Matter	Journal of Physics Condensed Matter, 4, 0.96% Journal of Physics Condensed Matter 4 publications, 0.96%
Journal of Molecular Liquids	Journal of Molecular Liquids, 4, 0.96% Journal of Molecular Liquids 4 publications, 0.96%
Carbon	Carbon, 4, 0.96% Carbon 4 publications, 0.96%
Journal of Nuclear Materials	Journal of Nuclear Materials, 4, 0.96% Journal of Nuclear Materials 4 publications, 0.96%
Chemical Science	Chemical Science, 4, 0.96% Chemical Science 4 publications, 0.96%
Nature Computational Science	Nature Computational Science, 3, 0.72% Nature Computational Science 3 publications, 0.72%
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Journal of Physics Energy	Journal of Physics Energy, 3, 0.72% Journal of Physics Energy 3 publications, 0.72%
	5 10 15 20 25 30 35 40

Citations by publishers

	10 20 30 40 50 60 70 80
Elsevier	Elsevier, 77, 18.51% Elsevier 77 publications, 18.51%
American Chemical Society (ACS)	American Chemical Society (ACS), 68, 16.35% American Chemical Society (ACS) 68 publications, 16.35%
Springer Nature	Springer Nature, 53, 12.74% Springer Nature 53 publications, 12.74%
American Institute of Physics (AIP)	American Institute of Physics (AIP), 51, 12.26% American Institute of Physics (AIP) 51 publications, 12.26%
American Physical Society (APS)	American Physical Society (APS), 50, 12.02% American Physical Society (APS) 50 publications, 12.02%
Royal Society of Chemistry (RSC)	Royal Society of Chemistry (RSC), 36, 8.65% Royal Society of Chemistry (RSC) 36 publications, 8.65%
IOP Publishing	IOP Publishing, 24, 5.77% IOP Publishing 24 publications, 5.77%
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 13, 3.13% Multidisciplinary Digital Publishing Institute (MDPI) 13 publications, 3.13%
Wiley	Wiley, 11, 2.64% Wiley 11 publications, 2.64%
Cambridge University Press	Cambridge University Press, 9, 2.16% Cambridge University Press 9 publications, 2.16%
Taylor & Francis	Taylor & Francis, 3, 0.72% Taylor & Francis 3 publications, 0.72%
American Society of Civil Engineers (ASCE)	American Society of Civil Engineers (ASCE), 2, 0.48% American Society of Civil Engineers (ASCE) 2 publications, 0.48%
Annual Reviews	Annual Reviews, 2, 0.48% Annual Reviews 2 publications, 0.48%
ASME	ASME, 1, 0.24% ASME 1 publication, 0.24%
Frontiers Media S.A.	Frontiers Media S.A., 1, 0.24% Frontiers Media S.A. 1 publication, 0.24%
Materials Research Society	Materials Research Society, 1, 0.24% Materials Research Society 1 publication, 0.24%
Chinese Ceramic Society	Chinese Ceramic Society, 1, 0.24% Chinese Ceramic Society 1 publication, 0.24%
Chemical Industry Press	Chemical Industry Press, 1, 0.24% Chemical Industry Press 1 publication, 0.24%
Beilstein-Institut	Beilstein-Institut, 1, 0.24% Beilstein-Institut 1 publication, 0.24%
Pleiades Publishing	Pleiades Publishing, 1, 0.24% Pleiades Publishing 1 publication, 0.24%
Walter de Gruyter	Walter de Gruyter, 1, 0.24% Walter de Gruyter 1 publication, 0.24%
Proceedings of the National Academy of Sciences (PNAS)	Proceedings of the National Academy of Sciences (PNAS), 1, 0.24% Proceedings of the National Academy of Sciences (PNAS) 1 publication, 0.24%
Oxford University Press	Oxford University Press, 1, 0.24% Oxford University Press 1 publication, 0.24%
	10 20 30 40 50 60 70 80

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GOST Copy

Zuo Y. et al. Performance and Cost Assessment of Machine Learning Interatomic Potentials // Journal of Physical Chemistry A. 2020. Vol. 124. No. 4. pp. 731-745.

GOST all authors (up to 50) Copy

Zuo Y., Chen C., Li X. G., Deng Z., Chen Y., Behler J., Csányi G., Shapeev A. V., Thompson A., Wood M. A., Ong S. P. Performance and Cost Assessment of Machine Learning Interatomic Potentials // Journal of Physical Chemistry A. 2020. Vol. 124. No. 4. pp. 731-745.

RIS |

Cite this

RIS Copy

TY - JOUR

DO - 10.1021/acs.jpca.9b08723

UR - https://doi.org/10.1021%2Facs.jpca.9b08723

TI - Performance and Cost Assessment of Machine Learning Interatomic Potentials

T2 - Journal of Physical Chemistry A

AU - Wood, Mitchell A.

AU - Zuo, Yunxing

AU - Chen, Chi-Hua

AU - Chen, Yiming

AU - Behler, Jörg

AU - Ong, Shyue Ping

AU - Li, Xiang Guo

AU - Deng, Zhi

AU - Csányi, G.

AU - Shapeev, Alexander V.

AU - Thompson, Aidan

PY - 2020

DA - 2020/01/09 00:00:00

PB - American Chemical Society (ACS)

SP - 731-745

IS - 4

VL - 124

SN - 1089-5639

SN - 1520-5215

ER -

BibTex |

Cite this

BibTex Copy

@article{2020_Zuo,

author = {Mitchell A. Wood and Yunxing Zuo and Chi-Hua Chen and Yiming Chen and Jörg Behler and Shyue Ping Ong and Xiang Guo Li and Zhi Deng and G. Csányi and Alexander V. Shapeev and Aidan Thompson},

title = {Performance and Cost Assessment of Machine Learning Interatomic Potentials},

journal = {Journal of Physical Chemistry A},

year = {2020},

volume = {124},

publisher = {American Chemical Society (ACS)},

month = {jan},

url = {https://doi.org/10.1021%2Facs.jpca.9b08723},

number = {4},

pages = {731--745},

doi = {10.1021/acs.jpca.9b08723}

}

MLA

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MLA Copy

Zuo, Yunxing, et al. “Performance and Cost Assessment of Machine Learning Interatomic Potentials.” Journal of Physical Chemistry A, vol. 124, no. 4, Jan. 2020, pp. 731-745. https://doi.org/10.1021%2Facs.jpca.9b08723.

Found error?

Publisher

American Chemical Society (ACS)

Journal

Journal of Physical Chemistry A

Quartile SCImago

Quartile WOS

Impact factor

2.9

ISSN

10895639 (Print)
15205215 (Electronic)

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