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 CoCrFeMnNiV-Al 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.
@article{arxiv.2111.03591,
title = {Towards Stacking Fault Energy Engineering in FCC High Entropy Alloys},
author = {Tasneem Khan and Tanner Kirk and Guillermo Vazquez and Prashant Singh and A V Smirnov and Duane D Johnson and Khaleed Youssef and Raymundo Arroyave},
journal= {arXiv preprint arXiv:2111.03591},
year = {2022}
}