Developing machine-learning potentials to study properties of the tritium formation and diffusivity in pure and defective Zircaloy-4 getters

Abstract

Objective of this work was to study the formation of Sn impurity and hydride phases in Zr and study their impacts on the diffusion kinetics of 3 H by using DFT based ML approach. By implementing deep neural potential (DNP) technique, we developed potential for Zr-H and Sn impurity systems and validated the DNP by using DFT results. We found that by implementing machine learning approach, it is possible to achieve accuracy comparable to DFT level for the more realistic models by using less computational time and resources.