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

2024/08/12 by Ali K. Shargh, Shargh, A. K., Christopher D. Stiles +3
Engineering · Materials Science · #Additive Manufacturing Materials and Processes #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Welding Techniques and Residual Stresses

paper · pdf · doi:10.48550/arxiv.2408.06237

openalex publication_date 2024/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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