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 longstanding deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.
9 More- Received 20 August 2019
- Corrected 11 March 2020
DOI:https://doi.org/10.1103/PhysRevB.100.144105
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