Towards stacking fault energy engineering in FCC high entropy alloys

Stacking Fault Energy (SFE) is an intrinsic alloy property that governs much of the plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for many years as a key intrinsic mechanical property, its inference via experimental observations or prediction using, for example, computationally intensive first-principles methods is challenging. This difficulty precludes the explicit use of SFE as an alloy design parameter. In this work, we combine DFT calculations (with necessary configurational averaging), machine-learning (ML) and physics-based models to predict the SFE in the fcc CoCrFeMnNiVAl high-entropy alloy space. The best-performing ML model is capable of accurately predicting the SFE of arbitrary compositions within this 7-element system. This efficient model along with a recently developed model to estimate intrinsic strength of fcc HEAs is used to explore the strength-SFE Pareto front, predicting new-candidate alloys with particularly interesting mechanical behavior.(c) 2021 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Automated summary

Stacking Fault Energy (SFE), an intrinsic property of alloys, plays a crucial role in governing the plastic deformation mechanisms in fcc alloys. In this study, a combination of DFT calculations, machine learning, and physics-based models is utilized to predict the SFE in the high-entropy alloy space. The developed model accurately predicts the SFE of arbitrary compositions and enables the exploration of new alloys with interesting mechanical behavior.