Machine-learning interatomic potential for radiation damage effects in bcc-iron

Y Wang and JB Liu and JH Li and JN Mei and ZC Li and WS Lai and F Xue, COMPUTATIONAL MATERIALS SCIENCE, 202, 110960 (2022).

DOI: 10.1016/j.commatsci.2021.110960

We introduce a machine-learning interatomic potential for bcc iron based on the moment tensor potential framework and a hybridization scheme of distinct sub-potentials. With an orientation on radiation damage effects, the potential shows good transferability from properties relevant to collision cascade to those relevant to plasticity. Specifically, the potential accurately reproduces the short-range repulsive interactions, the generalized stacking fault energies, the dislocation core structures and the formation energies of defect clusters. The general purposed applicability of the potential enables simulation of radiation damage effects in bcc iron with an accurate and an unprecedentedly unified theoretical model.

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