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

## TL;DR

This paper introduces a hybrid deep learning potential model that accurately simulates irradiation damage in materials, capturing physics at atomic scales and outperforming traditional potentials in aluminum simulations.

## Contribution

A novel hybrid scheme combining ZBL potential with a deep learning model for improved irradiation damage simulations in materials.

## Key findings

- Better prediction of defect formation energies
- More accurate simulation of collision cascades
- Enhanced modeling of residual point defects

## Abstract

We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The resulting DP-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 aluminium 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 its variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up new opportunities to solve the predicament of lacking accurate potentials for enormous newly-discovered materials in the irradiation effect field.