Hydrogen diffusion in zirconium hydrides from on-the-fly machine learning molecular dynamics

Abstract

Reactor pressure vessels and fuel cladding tubes have repeatedly failed due to zirconium hydrides. Zirconium hydride precipitation and growth are directly affected by hydrogen atom transport properties, which would make nuclear fuel storage less safe over long periods of time. Herein, we employ first-principles calculations to investigate the hydrogen diffusion mechanism in zirconium hydrides, utilizing on-the-fly machine learning force field molecular dynamics. It is verified that the machine learning force field can accurately describe the hydrogen atomic diffusion properties in zirconium hydrides at several temperatures and compositions. The atomic migration paths of hydrogen in zirconium hydrides as well as their barriers and pre-factors are also calculated. According to our results, the temperature and composition of zirconium hydrides affect the microscopic dynamical behavior of hydrogen.