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

Abstract

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.

Authors:
ORCiD logo [1];  [2];  [1]
+ Show Author Affiliations
  1. Inst. for Advanced Studies in Basic Sciences, Zanjan (Iran)
  2. Cornell Univ., Ithaca, NY (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1543873
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 149; Journal Issue: 12; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS; Chemistry; Physics

Citation Formats

Rostami, Samare, Amsler, Maximilian, and Ghasemi, S. Alireza. Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems. United States: N. p., 2018. Web. doi:10.1063/1.5040005.
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Rostami, Samare, Amsler, Maximilian, & Ghasemi, S. Alireza. Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems. United States. https://doi.org/10.1063/1.5040005
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Rostami, Samare, Amsler, Maximilian, and Ghasemi, S. Alireza. Tue . "Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems". United States. https://doi.org/10.1063/1.5040005. https://www.osti.gov/servlets/purl/1543873.
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@article{osti_1543873,
title = {Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems},
author = {Rostami, Samare and Amsler, Maximilian and Ghasemi, S. Alireza},
abstractNote = {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.},
doi = {10.1063/1.5040005},
journal = {Journal of Chemical Physics},
number = 12,
volume = 149,
place = {United States},
year = {Tue Sep 25 00:00:00 EDT 2018},
month = {Tue Sep 25 00:00:00 EDT 2018}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1063/1.5040005
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Citation Metrics:
Cited by: 18 works
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Figures / Tables:

FIG. 1 FIG. 1: (a) The convergence of the RMSE with the number of epochs in an ANN training process. The inset shows the predicted error as a function of training set size on a log-log scale at a fixed number of 20 epochs. Note the overall linear behavior, as generally expectedmore » for supervised machine learning models. (b) The distribution of the final RMSE as a function of the dimensionality in the compositional space, i.e., with different numbers of chemical species.« less
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    Works referencing / citing this record:

    Atomic partial charge predictions for furanoses by random forest regression with atom type symmetry function
    journal, January 2020

    Anharmonic thermodynamics of vacancies using a neural network potential
    journal, September 2019

    Anharmonic Thermodynamics of Vacancies Using a Neural Network Potential
    text, January 2019

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      Figures / Tables found in this record:

      FIG. 1 (p. 4)figure
      FIG. 2 (p. 5)figure
      FIG. 3 (p. 6)figure
      TABLE I (p. 6)table
      TABLE II (p. 6)table
      TABLE III (p. 7)table
      FIG. 4 (p. 8)figure
      TABLE IV (p. 8)table
      TABLE V (p. 8)table
      FIG. 5 (p. 9)figure
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