First-Principles-Based Machine-Learning Molecular Dynamics for Crystalline Polymers with van der Waals Interactions

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.