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Article: Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds

TitleDevelopment of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds
Authors
Issue Date2019
Citation
Physical Review B, 2019, v. 100, n. 17, article no. 174101 How to Cite?
AbstractInteratomic potentials based on neural-network machine learning (ML) approach to address the long-standing challenge of accuracy versus efficiency in molecular-dynamics simulations have recently attracted a great deal of interest. Here, utilizing Pd-Si system as a prototype, we extend the development of neural-network ML potentials to compounds exhibiting various types of bonding characteristics. The ML potential is trained by fitting to the energies and forces of both liquid and crystal structures first-principles calculations based on density-functional theory (DFT). We show that the generated ML potential captures the structural features and motifs in Pd82Si18 and Pd75Si25 liquids more accurately than the existing interatomic potential based on embedded-atom method (EAM). The ML potential also describes the solid-liquid interface of these systems very well. Moreover, while the existing EAM potential fails to describe the relative energies of various crystalline structures and predict wrong ground-state structures at Pd3Si and Pd9Si2 composition, the developed ML potential predicts correctly the ground-state structures from genetic algorithm search. The efficient ML potential with DFT accuracy from our study will provide a promising scheme for accurate atomistic simulations of structures and dynamics of complex Pd-Si system.
Persistent Identifierhttp://hdl.handle.net/10722/318799
ISSN
2023 Impact Factor: 3.2
2023 SCImago Journal Rankings: 1.345
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWen, Tongqi-
dc.contributor.authorWang, Cai Zhuang-
dc.contributor.authorKramer, M. J.-
dc.contributor.authorSun, Yang-
dc.contributor.authorYe, Beilin-
dc.contributor.authorWang, Haidi-
dc.contributor.authorLiu, Xueyuan-
dc.contributor.authorZhang, Chao-
dc.contributor.authorZhang, Feng-
dc.contributor.authorHo, Kai Ming-
dc.contributor.authorWang, Nan-
dc.date.accessioned2022-10-11T12:24:35Z-
dc.date.available2022-10-11T12:24:35Z-
dc.date.issued2019-
dc.identifier.citationPhysical Review B, 2019, v. 100, n. 17, article no. 174101-
dc.identifier.issn2469-9950-
dc.identifier.urihttp://hdl.handle.net/10722/318799-
dc.description.abstractInteratomic potentials based on neural-network machine learning (ML) approach to address the long-standing challenge of accuracy versus efficiency in molecular-dynamics simulations have recently attracted a great deal of interest. Here, utilizing Pd-Si system as a prototype, we extend the development of neural-network ML potentials to compounds exhibiting various types of bonding characteristics. The ML potential is trained by fitting to the energies and forces of both liquid and crystal structures first-principles calculations based on density-functional theory (DFT). We show that the generated ML potential captures the structural features and motifs in Pd82Si18 and Pd75Si25 liquids more accurately than the existing interatomic potential based on embedded-atom method (EAM). The ML potential also describes the solid-liquid interface of these systems very well. Moreover, while the existing EAM potential fails to describe the relative energies of various crystalline structures and predict wrong ground-state structures at Pd3Si and Pd9Si2 composition, the developed ML potential predicts correctly the ground-state structures from genetic algorithm search. The efficient ML potential with DFT accuracy from our study will provide a promising scheme for accurate atomistic simulations of structures and dynamics of complex Pd-Si system.-
dc.languageeng-
dc.relation.ispartofPhysical Review B-
dc.titleDevelopment of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1103/PhysRevB.100.174101-
dc.identifier.scopuseid_2-s2.0-85075184926-
dc.identifier.volume100-
dc.identifier.issue17-
dc.identifier.spagearticle no. 174101-
dc.identifier.epagearticle no. 174101-
dc.identifier.eissn2469-9969-
dc.identifier.isiWOS:000494024200001-

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