Segregation, ordering, and precipitation in WTaV-based concentrated refractory alloys

TL;DR

This study develops a machine-learned interatomic potential for W-Ta-Cr-V alloys and uses it to simulate segregation and precipitation phenomena, providing insights that align with experimental atom probe tomography results.

Contribution

It introduces an accurate machine learning model for simulating refractory alloy behavior and compares predictions with experimental data to understand segregation and precipitation.

Findings

CrV precipitates form semicoherent interfaces in WTaCrV

Simulations agree with atom probe tomography observations

Guides future alloy design by understanding segregation mechanisms

Abstract

Tungsten-based low-activation high-entropy alloys are possible candidates for next-generation fusion reactors due to their exceptional tolerance to irradiation, thermal loads, and stress. We develop an accurate and efficient machine-learned interatomic potential for the W-Ta-Cr-V system and use it in hybrid Monte Carlo molecular dynamics simulations of ordering and segregation to all common types of defects in WTaCrV. The predictions are compared to atom probe tomography analysis of segregation and precipitation in WTaCrV thin films. By also considering two other alloys, WTaV and MoNbTaVW, we are able to draw general conclusions about preferred segregation in refractory alloys and the reasons behind it, guiding future alloy design and elucidating experimental observations. We show that the experimentally observed CrV precipitates in WTaCrV form semicoherent bcc-to-bcc interfaces with the surrounding matrix, as coherent precipitates are not thermodynamically stable due to excessive lattice mismatch. The predictions from simulations align well with our atom probe tomography analysis as well as previous experimental observations.