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Kim, Kwang S.
Center for Superfunctional Materials (CSM)
Research Interests
  • Theoretical/experimental nanosciences, molecular electronics spectroscopy, energy materials

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Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons

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dc.contributor.author Hajibabaei, Amir ko
dc.contributor.author Ha, Miran ko
dc.contributor.author Pourasad, Saeed ko
dc.contributor.author Kim, Junu ko
dc.contributor.author Kim, Kwang S. ko
dc.date.available 2021-12-24T00:43:48Z -
dc.date.created 2021-12-09 ko
dc.date.issued 2021-10 ko
dc.identifier.citation JOURNAL OF PHYSICAL CHEMISTRY A, v.125, no.42, pp.9414 - 9420 ko
dc.identifier.issn 1089-5639 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/55351 -
dc.description.abstract Machine learning (ML) interatomic potentials (ML-IAPs) are generated for alkane and polyene hydrocarbons using on-the-fly adaptive sampling and a sparse Gaussian process regression (SGPR) algorithm. The ML model is generated based on the PBE+D3 level of density functional theory (DFT) with molecular dynamics (MD) for small alkane and polyene molecules. Intermolecular interactions are also trained with clusters and condensed phases of small molecules. It shows excellent transferability to long alkanes and closely describes the ab inito potential energy surface for polyenes. Simulation of liquid ethane also shows reasonable agreement with experimental reports. This is a promising initiative toward a universal ab initio quality force-field for hydrocarbons and organic molecules. ko
dc.language 영어 ko
dc.publisher AMER CHEMICAL SOC ko
dc.title Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85118141843 ko
dc.identifier.wosid 000713417800016 ko
dc.type.rims ART ko
dc.identifier.doi 10.1021/acs.jpca.1c05819 ko
dc.identifier.url https://pubs.acs.org/doi/10.1021/acs.jpca.1c05819 ko
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