Utilizing a machine-learned potential to explore enhanced radiation tolerance in the MoNbTaVW high-entropy alloy
Jiahui Liu, Jesper Byggmastar, Zheyong Fan, Bing Bai, Ping Qian, Yanjing Su

TL;DR
This study develops a machine-learned interatomic potential for MoNbTaVW high-entropy alloy, enabling large-scale molecular dynamics simulations that reveal its superior radiation tolerance compared to pure tungsten, due to defect dynamics like subcascade splitting.
Contribution
The paper introduces an efficient machine-learned potential for MoNbTaVW HEA, allowing detailed radiation damage simulations and revealing mechanisms behind its enhanced radiation resistance.
Findings
MoNbTaVW HEA shows more surviving Frenkel pairs than W.
Fewer and smaller interstitial clusters are observed in HEA.
Subcascade splitting reduces interstitial cluster formation in HEA.
Abstract
High-entropy alloys (HEAs) based on tungsten (W) have emerged as promising candidates for plasma-facing components in future fusion reactors, owing to their excellent irradiation resistance. In this study, we construct an efficient machine-learned interatomic potential for the MoNbTaVW quinary system. This potential achieves computational speeds comparable to the embedded-atom method (EAM) potential, allowing us to conduct a comprehensive investigation of the primary radiation damage through molecular dynamics simulations. Threshold displacement energies (TDEs) in the MoNbTaVW HEA are investigated and compared with pure metals. A series of displacement cascade simulations at primary knock-on atom energies ranging from 10 to 150 keV reveal significant differences in defect generation and clustering between MoNbTaVW HEA and pure W. In HEAs, we observe more surviving Frenkel pairs (FPs)…
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Taxonomy
TopicsNuclear Materials and Properties · Additive Manufacturing Materials and Processes · High Entropy Alloys Studies
