Efficient atomistic simulations of radiation damage in W and W-Mo using machine-learning potentials

TL;DR

This study validates a faster machine-learning potential, tabGAP, for simulating radiation damage in tungsten and tungsten-molybdenum alloys, showing comparable accuracy and significantly improved computational efficiency.

Contribution

The paper introduces and validates tabGAP, a faster machine-learning interatomic potential, for modeling radiation damage in W and W-Mo alloys, demonstrating its accuracy and efficiency.

Findings

tabGAP is two orders of magnitude faster than GAP

W-Mo alloys show similar defect survival as pure W

W-Mo exhibits more efficient defect recombination

Abstract

The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this study, we seek to validate a faster version of GAP, known as tabulated GAP (tabGAP), by modelling primary radiation damage in 50-50 W-Mo alloys and pure W using classical molecular dynamics. We find that W-Mo exhibits a similar number of surviving defects as in pure W. We also observe W-Mo to possess both more efficient recombination of defects produced during the initial phase of the cascades, and in some cases, unlike pure W, recombination of all defects after the cascades cooled down. Furthermore, we observe that the tabGAP is two orders of magnitude faster than GAP, but produces a comparable number of surviving defects and cluster sizes. A small difference is noted in the fraction of interstitials that are bound into clusters.