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

J Byggmastar and A Hamedani and K Nordlund and F Djurabekova, PHYSICAL REVIEW B, 100, 144105 (2019).

DOI: 10.1103/PhysRevB.100.144105

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 longstanding deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.

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