Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten
Jiahui Liu, Jesper Byggmastar, Zheyong Fan, Ping Qian, and Yanjing Su

TL;DR
This paper develops an efficient machine-learning interatomic potential for tungsten, enabling large-scale molecular dynamics simulations to study primary radiation damage, revealing surface effects on defect formation.
Contribution
It introduces the NEP-ZBL framework combining neuroevolution potentials with ZBL screening, allowing accurate and efficient large-scale radiation damage simulations in tungsten.
Findings
Bulk tungsten damage aligns with existing models.
Foil damage shows larger vacancy clusters and fewer interstitials.
Surface presence significantly affects defect formation.
Abstract
Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning based interatomic potentials have shown sufficiently high accuracy for radiation damage simulations, but most existing ones are still not efficient enough to model high-energy collision cascades with sufficiently large space and time scales. To this end, we here extend the highly efficient neuroevolution potential (NEP) framework by combining it with the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential, obtaining a NEP-ZBL framework. We train a NEP-ZBL model for tungsten and demonstrate its accuracy in terms of the elastic properties, melting point, and various energetics of defects that are relevant for radiation damage. We then perform large-scale molecular dynamics simulations with the…
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Taxonomy
TopicsNuclear Materials and Properties · Nuclear Physics and Applications · Fusion materials and technologies
