Deep Learning Accelerated Phase Prediction of Refractory Multi-Principal Element Alloys

TL;DR

This paper introduces a deep learning framework trained on CALPHAD data to accurately predict the phase stability of refractory multi-principal-element alloys, aiding high-temperature material design.

Contribution

It presents a novel deep learning approach for phase prediction in RMPEAs, achieving high accuracy and addressing out-of-domain performance issues.

Findings

Achieves approximately 90% accuracy in phase prediction.

Identifies causes of low out-of-domain performance.

Proposes strategies to improve model generalization.

Abstract

The tunability of the mechanical properties of refractory multi-principal-element alloys (RMPEAs) make them attractive for numerous high-temperature applications. It is well-established that the phase stability of RMPEAs control their mechanical properties. In this study, we develop a deep learning framework that is trained on a CALPHAD-derived database that is predictive of RMPEAs phases with high accuracy up to eight phases within the elemental space of Ti, Fe, Al, V, Ni, Nb, and Zr with an accuracy of approximately 90%. We further investigate the causes for the low out of domain performance of the deep learning models in predicting phases of RMPEA with new elemental sets and propose a strategy to mitigate this performance shortfall.