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• W3174990168 abstract "Machine-learning (ML) techniques have drawn an ever-increasing focus as they enable high-throughput screening and multiscale prediction of material properties. Especially, ML force fields (FFs) of quantum mechanical accuracy are expected to play a central role for the purpose. The construction of ML-FFs for polymers is, however, still in its infancy due to the formidable configurational space of its composing atoms. Here, we demonstrate the effective development of ML-FFs using kernel functions and a Gaussian process for an organic polymer, polytetrafluoroethylene (PTFE), with a data set acquired by first-principles calculations and ab initio molecular dynamics (AIMD) simulations. Even though the training data set is sampled only with short PTFE chains, structures of longer chains optimized by our ML-FF show an excellent consistency with density functional theory calculations. Furthermore, when integrated with molecular dynamics simulations, the ML-FF successfully describes various physical properties of a PTFE bundle, such as a density, melting temperature, coefficient of thermal expansion, and Young’s modulus." @default.
• W3174990168 created "2021-07-05" @default.
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• W3174990168 date "2021-06-24" @default.
• W3174990168 modified "2023-10-17" @default.
• W3174990168 title "First-Principles-Based Machine-Learning Molecular Dynamics for Crystalline Polymers with van der Waals Interactions" @default.
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• W3174990168 doi "https://doi.org/10.1021/acs.jpclett.1c01140" @default.
• W3174990168 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34165310" @default.
• W3174990168 hasPublicationYear "2021" @default.
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