Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems

Rostami, Samare; Amsler, Maximilian; Ghasemi, S. Alireza

doi:10.1063/1.5040005

Title: Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems

Journal Article · 25 September 2018 · Journal of Chemical Physics

DOI: https://doi.org/10.1063/1.5040005 · OSTI ID:1543873

^[1]; Amsler, Maximilian ^[2]; Ghasemi, S. Alireza ^[1]

Inst. for Advanced Studies in Basic Sciences, Zanjan (Iran)
Cornell Univ., Ithaca, NY (United States)

Current machine-learning methods to reproduce ab initio potential energy landscapes suffer from an unfavorable computational scaling with respect to the number of chemical species. In this work, we propose a new approach by using optimized symmetry functions to explore similarities of structures in multicomponent systems in order to yield linear complexity. Here, we combine these symmetry functions with the charge equilibration via neural network technique, a reliable artificial neural network potential for ionic materials, and apply this method to study alkali-halide materials MX with 6 chemical species (M = {Li, Na, K} and X = {F, Cl, Br}). Our results show that our approach provides good agreement both with experimental and DFT reference data of many physical and structural properties for any chemical combination.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1543873

Journal Information:: Journal of Chemical Physics, Vol. 149, Issue 12; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 18 works

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