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Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations

[Image: see text] A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy. The method to distribute pairs a...

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Autores principales: Broad, Jack, Wheatley, Richard J., Graham, Richard S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339671/
https://www.ncbi.nlm.nih.gov/pubmed/37368843
http://dx.doi.org/10.1021/acs.jctc.3c00113
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author Broad, Jack
Wheatley, Richard J.
Graham, Richard S.
author_facet Broad, Jack
Wheatley, Richard J.
Graham, Richard S.
author_sort Broad, Jack
collection PubMed
description [Image: see text] A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy. The method to distribute pairs and triplets between processes is general to all potentials. Results are presented for a simulation box of argon, including full box and atom displacement calculations, which are relevant to Monte Carlo simulation. Data on speed-up are presented for up to 120 processes across four nodes. A 4-fold speed-up is observed over five processes, extending to 20-fold over 40 processes and 30-fold over 120 processes.
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spelling pubmed-103396712023-07-14 Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations Broad, Jack Wheatley, Richard J. Graham, Richard S. J Chem Theory Comput [Image: see text] A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy. The method to distribute pairs and triplets between processes is general to all potentials. Results are presented for a simulation box of argon, including full box and atom displacement calculations, which are relevant to Monte Carlo simulation. Data on speed-up are presented for up to 120 processes across four nodes. A 4-fold speed-up is observed over five processes, extending to 20-fold over 40 processes and 30-fold over 120 processes. American Chemical Society 2023-06-27 /pmc/articles/PMC10339671/ /pubmed/37368843 http://dx.doi.org/10.1021/acs.jctc.3c00113 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Broad, Jack
Wheatley, Richard J.
Graham, Richard S.
Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations
title Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations
title_full Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations
title_fullStr Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations
title_full_unstemmed Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations
title_short Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations
title_sort parallel implementation of nonadditive gaussian process potentials for monte carlo simulations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339671/
https://www.ncbi.nlm.nih.gov/pubmed/37368843
http://dx.doi.org/10.1021/acs.jctc.3c00113
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