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Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles

We have investigated Machine Learning Interatomic Potentials in application to the properties of gold nanoparticles through the DeePMD package, using data generated with the ab-initio VASP program. Benchmarking was carried out on Au [Formula: see text] nanoclusters against ab-initio molecular dynami...

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Detalles Bibliográficos
Autores principales: Fronzi, Marco, Amos, Roger D., Kobayashi, Rika, Matsumura, Naoki, Watanabe, Kenta, Morizawa, Rafael K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655512/
https://www.ncbi.nlm.nih.gov/pubmed/36364667
http://dx.doi.org/10.3390/nano12213891
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author Fronzi, Marco
Amos, Roger D.
Kobayashi, Rika
Matsumura, Naoki
Watanabe, Kenta
Morizawa, Rafael K.
author_facet Fronzi, Marco
Amos, Roger D.
Kobayashi, Rika
Matsumura, Naoki
Watanabe, Kenta
Morizawa, Rafael K.
author_sort Fronzi, Marco
collection PubMed
description We have investigated Machine Learning Interatomic Potentials in application to the properties of gold nanoparticles through the DeePMD package, using data generated with the ab-initio VASP program. Benchmarking was carried out on Au [Formula: see text] nanoclusters against ab-initio molecular dynamics simulations and show we can achieve similar accuracy with the machine learned potential at far reduced cost using LAMMPS. We have been able to reproduce structures and heat capacities of several isomeric forms. Comparison of our workflow with similar ML-IP studies is discussed and has identified areas for future improvement.
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spelling pubmed-96555122022-11-15 Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles Fronzi, Marco Amos, Roger D. Kobayashi, Rika Matsumura, Naoki Watanabe, Kenta Morizawa, Rafael K. Nanomaterials (Basel) Article We have investigated Machine Learning Interatomic Potentials in application to the properties of gold nanoparticles through the DeePMD package, using data generated with the ab-initio VASP program. Benchmarking was carried out on Au [Formula: see text] nanoclusters against ab-initio molecular dynamics simulations and show we can achieve similar accuracy with the machine learned potential at far reduced cost using LAMMPS. We have been able to reproduce structures and heat capacities of several isomeric forms. Comparison of our workflow with similar ML-IP studies is discussed and has identified areas for future improvement. MDPI 2022-11-03 /pmc/articles/PMC9655512/ /pubmed/36364667 http://dx.doi.org/10.3390/nano12213891 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fronzi, Marco
Amos, Roger D.
Kobayashi, Rika
Matsumura, Naoki
Watanabe, Kenta
Morizawa, Rafael K.
Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles
title Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles
title_full Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles
title_fullStr Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles
title_full_unstemmed Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles
title_short Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles
title_sort evaluation of machine learning interatomic potentials for the properties of gold nanoparticles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655512/
https://www.ncbi.nlm.nih.gov/pubmed/36364667
http://dx.doi.org/10.3390/nano12213891
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