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 andab initiomolecular 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.
|Number of pages||7|
|Journal||Journal of Physical Chemistry Letters|
|Publication status||Published - 2021|
Bibliographical noteFunding Information:
This research was supported by the Research and Development Program of Korea Institute of Energy Research (C1-2415 and C1-2447), the Global Frontier Program through the Global Frontier Hybrid Interface Materials (GFHIM) of National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Project No. 2013M3A6B1078882), and Creative Materials Discovery Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF2017M3D1A1039287).
© 2021 American Chemical Society
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Physical and Theoretical Chemistry