Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: defects and segregation

TL;DR

This study develops a machine-learned interatomic potential for a refractory high-entropy alloy, revealing defect behaviors, segregation patterns, and ordering phenomena that differ significantly from pure tungsten, with implications for radiation resistance.

Contribution

We introduce an efficient machine-learned potential for MoNbTaVW alloy and analyze its defect and segregation behaviors, providing new insights into its radiation response and defect dynamics.

Findings

Vanadium segregates to interstitial-rich regions and forms small dislocation loops.

Niobium segregates to void surfaces due to its size and surface energy.

Defect recombination is promoted over clustering in the alloy, differing from tungsten.

Abstract

We develop a fast and accurate machine-learned interatomic potential for the Mo-Nb-Ta-V-W quinary system and use it to study segregation and defects in the body-centred cubic refractory high-entropy alloy MoNbTaVW. In the bulk alloy, we observe clear ordering of mainly Mo-Ta and V-W binaries at low temperatures. In damaged crystals, our simulations reveal clear segregation of vanadium, the smallest atom in the alloy, to compressed interstitial-rich regions like radiation-induced dislocation loops. Vanadium also dominates the population of single self-interstitial atoms. In contrast, due to its larger size and low surface energy, niobium segregates to spacious regions like the inner surfaces of voids. When annealing samples with supersaturated concentrations of defects, we find that in complete contrast to W, interstitial atoms in MoNbTaVW cluster to create only small ($\sim 1$ nm) experimentally invisible dislocation loops enriched by vanadium. By comparison to W, we explain this by the reduced but three-dimensional migration of interstitials, the immobility of dislocation loops, and the increased mobility of vacancies in the high-entropy alloy, which together promote defect recombination over clustering.