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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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Title
Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons
Author
Hajibabaei, AmirHa, MiranPourasad, SaeedKim, JunuKim, Kwang S.
Issue Date
2021-10
Publisher
AMER CHEMICAL SOC
Citation
JOURNAL OF PHYSICAL CHEMISTRY A, v.125, no.42, pp.9414 - 9420
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/55351
URL
https://pubs.acs.org/doi/10.1021/acs.jpca.1c05819
DOI
10.1021/acs.jpca.1c05819
ISSN
1089-5639
Appears in Collections:
CHM_Journal Papers
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