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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9655512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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