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DC Field | Value | Language |
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dc.citation.number | 34 | - |
dc.citation.startPage | 344007 | - |
dc.citation.title | JOURNAL OF PHYSICS-CONDENSED MATTER | - |
dc.citation.volume | 34 | - |
dc.contributor.author | Hajibabaei, Amir | - |
dc.contributor.author | Umer, Muhammad | - |
dc.contributor.author | Anand, Rohit | - |
dc.contributor.author | Ha, Miran | - |
dc.contributor.author | Kim, Kwang S. | - |
dc.date.accessioned | 2023-12-21T14:08:00Z | - |
dc.date.available | 2023-12-21T14:08:00Z | - |
dc.date.created | 2022-07-12 | - |
dc.date.issued | 2022-06 | - |
dc.description.abstract | We apply on-the-fly machine learning potentials (MLPs) using the sparse Gaussian process regression (SGPR) algorithm for fast optimization of atomic structures. Great acceleration is achieved even in the context of a single local optimization. Although for finding the exact local minimum, due to limited accuracy of MLPs, switching to another algorithm may be needed. For random gold clusters, the forces are reduced to similar to 0.1 eV angstrom(-1) within less than ten first-principles (FP) calculations. Because of highly transferable MLPs, this algorithm is specially suitable for global optimization methods such as random or evolutionary structure searching or basin hopping. This is demonstrated by sequential optimization of random gold clusters for which, after only a few optimizations, FP calculations were rarely needed. | - |
dc.identifier.bibliographicCitation | JOURNAL OF PHYSICS-CONDENSED MATTER, v.34, no.34, pp.344007 | - |
dc.identifier.doi | 10.1088/1361-648X/ac76ff | - |
dc.identifier.issn | 0953-8984 | - |
dc.identifier.scopusid | 2-s2.0-85133576731 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/59077 | - |
dc.identifier.wosid | 000817735500001 | - |
dc.language | 영어 | - |
dc.publisher | IOP Publishing Ltd | - |
dc.title | Fast atomic structure optimization with on-the-fly sparse Gaussian process potentials * | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Physics, Condensed Matter | - |
dc.relation.journalResearchArea | Physics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | structure optimization | - |
dc.subject.keywordAuthor | machine learning potentials | - |
dc.subject.keywordAuthor | sparse Gaussian process potentials | - |
dc.subject.keywordPlus | SELECTIVE OXIDATION | - |
dc.subject.keywordPlus | GLOBAL OPTIMIZATION | - |
dc.subject.keywordPlus | GOLD NANOCLUSTERS | - |
dc.subject.keywordPlus | GENETIC ALGORITHM | - |
dc.subject.keywordPlus | CLUSTERS | - |
dc.subject.keywordPlus | NANOPARTICLES | - |
dc.subject.keywordPlus | CHEMISTRY | - |
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