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

Fronzi, M; Amos, RD; Kobayashi, R; Matsumura, N; Watanabe, K; Morizawa, RK

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

Fronzi, M

Amos, RD Kobayashi, R Matsumura, N Watanabe, K Morizawa, RK

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Nanomaterials (Basel), 2022, 12, (21), pp. 3891
Issue Date:: 2022-11-03

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Field	Value	Language
dc.contributor.author	Fronzi, M https://orcid.org/0000-0001-7855-9216
dc.contributor.author	Amos, RD
dc.contributor.author	Kobayashi, R
dc.contributor.author	Matsumura, N
dc.contributor.author	Watanabe, K
dc.contributor.author	Morizawa, RK
dc.date.accessioned	2023-03-28T03:24:30Z
dc.date.available	2022-10-26
dc.date.available	2023-03-28T03:24:30Z
dc.date.issued	2022-11-03
dc.identifier.citation	Nanomaterials (Basel), 2022, 12, (21), pp. 3891
dc.identifier.issn	2079-4991
dc.identifier.issn	2079-4991
dc.identifier.uri	http://hdl.handle.net/10453/168688
dc.description.abstract	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 Au20 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.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation	http://purl.org/au-research/grants/arc/DP200101217
dc.relation.ispartof	Nanomaterials (Basel)
dc.relation.isbasedon	10.3390/nano12213891
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0912 Materials Engineering, 1007 Nanotechnology
dc.title	Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles.
dc.type	Journal Article
utslib.citation.volume	12
utslib.location.activity	Switzerland
utslib.for	0912 Materials Engineering
utslib.for	1007 Nanotechnology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-28T03:24:20Z
pubs.issue	21
pubs.publication-status	Published online
pubs.volume	12
utslib.citation.issue	21

Abstract:

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 Au20 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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http://hdl.handle.net/10453/168688