Machine-learning interatomic potential for radiation damage and defects in tungsten

TL;DR

This paper presents a new machine-learning interatomic potential for tungsten that accurately models radiation damage, collision cascades, and defect properties, enabling highly precise molecular dynamics simulations.

Contribution

The authors develop a Gaussian Approximation Potential for tungsten that improves upon existing models by accurately capturing collision dynamics, surface properties, and defect energetics.

Findings

Accurately models collision cascades in tungsten

Reproduces surface and defect energetics effectively

Enables high-precision radiation damage simulations

Abstract

We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self-interstitial clusters, which have been long-standing deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.