Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations

Krishnamoorthy, Aravind; Mishra, Ankit; Grabar, Nicholas; Baradwaj, Nitish; K. Kalia, Rajiv; Nakano, Aiichiro; Vashishta, Priya

doi:10.1016/j.cpc.2020.107337

Title: Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations

Journal Article · Tue Apr 28 00:00:00 EDT 2020 · Computer Physics Communications

DOI:https://doi.org/10.1016/j.cpc.2020.107337· OSTI ID:1802687

^[1];

^[1]; Grabar, Nicholas ^[1]; Baradwaj, Nitish ^[1]; K. Kalia, Rajiv ^[1];

^[1]; Vashishta, Priya ^[1]

Univ. of Southern California, Los Angeles, CA (United States)

Predictive Molecular Dynamics simulations of thermal transport require forcefields that can simultaneously reproduce several structural, thermodynamic and vibrational properties of materials like lattice constants, phonon density of states, and specific heat. This requires a multi-objective optimization approach for forcefield parameterization. Existing methodologies for forcefield parameterization use ad-hoc and empirical weighting schemes to convert this into a single-objective optimization problem. Here, we provide and describe software to perform multi-objective optimization of Stillinger–Weber forcefields (SWFF) for two-dimensional layered materials using the recently developed 3rd generation non-dominated sorting genetic algorithm (NSGA-III). NSGA-III converges to the set of optimal forcefields lying on the Pareto front in the multi-dimensional objective space. This set of forcefields is used for uncertainty quantification of computed thermal conductivity due to variability in the forcefield parameters. We demonstrate this new optimization scheme by constructing a SWFF for a representative two-dimensional material, 2H-MoSe₂ and quantifying the uncertainty in their computed thermal conductivity.

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Research Organization:: Univ. of Southern California, Los Angeles, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0014607

OSTI ID:: 1802687

Alternate ID(s):: OSTI ID: 1698075

Journal Information:: Computer Physics Communications, Vol. 254; ISSN 0010-4655

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations Krishnamoorthy, Aravind https://doi.org/10.17632/pbc6nb7hp9.1 The current record is supplemented by this dataset	dataset	January 2020
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Title: Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations

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