Modeling of metal nanoparticles: Development of neural-network interatomic potential inspired by features of the modified embedded-atom method

Wu, F; Min, H; Wen, Y; Chen, R; Zhao, Y; Ford, M; Shan, B

Modeling of metal nanoparticles: Development of neural-network interatomic potential inspired by features of the modified embedded-atom method

Wu, F Min, H Wen, Y Chen, R Zhao, Y Ford, M

Shan, B

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Publisher:: AMER PHYSICAL SOC
Publication Type:: Journal Article
Citation:: Physical Review B, 2020, 102, (14)
Issue Date:: 2020-10-15

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Field	Value	Language
dc.contributor.author	Wu, F
dc.contributor.author	Min, H
dc.contributor.author	Wen, Y
dc.contributor.author	Chen, R
dc.contributor.author	Zhao, Y
dc.contributor.author	Ford, M https://orcid.org/0000-0002-8595-5900
dc.contributor.author	Shan, B
dc.date.accessioned	2021-03-07T05:26:28Z
dc.date.available	2021-03-07T05:26:28Z
dc.date.issued	2020-10-15
dc.identifier.citation	Physical Review B, 2020, 102, (14)
dc.identifier.issn	2469-9950
dc.identifier.issn	2469-9969
dc.identifier.uri	http://hdl.handle.net/10453/146846
dc.description.abstract	© 2020 American Physical Society. Interatomic potential plays a key role in ensuring the accuracy and reliability of molecular-dynamics simulation results. While most empirical potentials are benchmarked against a set of carefully chosen bulk material properties, recent advances in machine learning have seen the emergence of neural-network-based mathematical potentials capable of describing highly complex potential energy surfaces for a variety of systems. We report here the development of a neural-network interatomic potential (NNIP) with modified embedded-atom method background density as fingerprint functions, which could accurately model the energetics of metallic nanoparticles and clusters (Cu as a representative example) widely used in catalysis. To appropriately account for the diverse chemical environments encountered in nanoparticles/nanoclusters, an extensive set of atomic configurations (totaling 18 084) were calculated using density-functional-theory (DFT) at the Perdew-Burke-Ernzerhof level. In addition to standard bulk properties such as cohesive energies and elastic constants, the sampled configurations also include a substantial number of differently oriented crystal facets and differently sized nanoparticles and nanoclusters, greatly expanding the value range of NNIP features that was otherwise quite limited. The complex energy potential surface of Cu can be faithfully reproduced, with an average error of 0.011 eV/at for energy states within 3 eV of the ground state. As an illustration, the developed NNIP is used to simulate the molecular dynamics of copper nanoparticles, and good agreement is achieved between DFT and the NNIP.
dc.language	English
dc.publisher	AMER PHYSICAL SOC
dc.relation.ispartof	Physical Review B
dc.relation.isbasedon	10.1103/PhysRevB.102.144107
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	02 Physical Sciences, 03 Chemical Sciences, 09 Engineering
dc.subject.classification	Fluids & Plasmas
dc.title	Modeling of metal nanoparticles: Development of neural-network interatomic potential inspired by features of the modified embedded-atom method
dc.type	Journal Article
utslib.citation.volume	102
utslib.for	02 Physical Sciences
utslib.for	03 Chemical Sciences
utslib.for	09 Engineering
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-07T05:26:25Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	102
utslib.citation.issue	14

Abstract:

© 2020 American Physical Society. Interatomic potential plays a key role in ensuring the accuracy and reliability of molecular-dynamics simulation results. While most empirical potentials are benchmarked against a set of carefully chosen bulk material properties, recent advances in machine learning have seen the emergence of neural-network-based mathematical potentials capable of describing highly complex potential energy surfaces for a variety of systems. We report here the development of a neural-network interatomic potential (NNIP) with modified embedded-atom method background density as fingerprint functions, which could accurately model the energetics of metallic nanoparticles and clusters (Cu as a representative example) widely used in catalysis. To appropriately account for the diverse chemical environments encountered in nanoparticles/nanoclusters, an extensive set of atomic configurations (totaling 18 084) were calculated using density-functional-theory (DFT) at the Perdew-Burke-Ernzerhof level. In addition to standard bulk properties such as cohesive energies and elastic constants, the sampled configurations also include a substantial number of differently oriented crystal facets and differently sized nanoparticles and nanoclusters, greatly expanding the value range of NNIP features that was otherwise quite limited. The complex energy potential surface of Cu can be faithfully reproduced, with an average error of 0.011 eV/at for energy states within 3 eV of the ground state. As an illustration, the developed NNIP is used to simulate the molecular dynamics of copper nanoparticles, and good agreement is achieved between DFT and the NNIP.

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http://hdl.handle.net/10453/146846