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

M Koskenniemi and J Byggmästar and K Nordlund and F Djurabekova, JOURNAL OF NUCLEAR MATERIALS, 577, 154325 (2023).

DOI: 10.1016/j.jnucmat.2023.154325

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 recom-bination 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.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

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