Reinforcement learning-guided long-timescale simulation of hydrogen transport in metals
Abstract
Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interests. In this work, we developed a long-timescale method using reinforcement learning that simulates diffusion processes. As a testbed, we simulate hydrogen diffusion in pure metals and a medium entropy alloy, CrCoNi, getting hydrogen diffusivity reasonably consistent with previous experiments. We also demonstrate that our method can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm using hydrogen migration to copper (111) surface sites as an example.
Keywords
Cite
@article{arxiv.2307.05394,
title = {Reinforcement learning-guided long-timescale simulation of hydrogen transport in metals},
author = {Hao Tang and Boning Li and Yixuan Song and Mengren Liu and Haowei Xu and Guoqing Wang and Heejung Chung and Ju Li},
journal= {arXiv preprint arXiv:2307.05394},
year = {2023}
}