Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 12, Issue 25, Pages 6000-6006Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.1c01140
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Funding
- Research and Development Program of Korea Institute of Energy Research [C1-2415, C1-2447]
- Global Frontier Program through the Global Frontier Hybrid Interface Materials (GFHIM) of National Research Foundation of Korea (NRF) - Ministry of Science and ICT [2013M3A6B1078882]
- Creative Materials Discovery Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [NRF2017M3D1A1039287]
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This study demonstrates the effective development of ML-FFs for the polymer PTFE, showing excellent consistency with density functional theory calculations for longer chain structures. When integrated with molecular dynamics simulations, the ML-FF successfully describes various physical properties of a PTFE bundle.
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.
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