Deep learning inter-atomic potential model for accurate irradiation damage simulations

Abstract

We propose a hybrid scheme that smoothly interpolates the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a deep learning potential energy model. The resulting deep potential-ZBL model can not only provide overall good performance on the predictions of near-equilibrium material properties but also capture the right physics when atoms are extremely close to each other, an event that frequently happens in computational simulations of irradiation damage events. We applied this scheme to the simulation of the irradiation damage processes in the face-centered-cubic aluminum system and found better descriptions in terms of the defect formation energy, evolution of collision cascades, displacement threshold energy, and residual point defects than the widely adopted ZBL modified embedded atom method potentials and their variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up extra opportunities to solve the predicament of lacking accurate potentials for enormous recently discovered materials in the irradiation effect field.