Analysis of Irradiation Damage in Alpha Iron Using Machine Learning Interatomic Potentials

Abstract

In reactor pressure vessel (RPV) steels, it is known that irradiated neutrons can embrittle the atoms that make up the RPV, resulting in the formation of defects (neutron irradiation embrittlement). In the analysis of irradiation damage, interatomic potentials that accurately satisfy such characteristics as diffusion energy of defects, repulsive threshold energy, and short-range interaction force are required. Recently, machine-learning-based interatomic potentials have been developed and shown to reproduce DFT results with high accuracy. In this study, we will perform irradiation damage analysis using the machine-learned interatomic potential and clarify the distribution of defects and the mechanism of defect generation due to irradiation.