  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.
