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 mechanical property, its inference via experimental observations or prediction using, example, computationally intensive first-principles methods challenging. This difficulty precludes explicit use design parameter. In this work, we combine DFT calculations (with necessary configurational averaging), machine-learning (ML) and physics-based models to predict CoCrFeMnNiV-Al high-entropy space. The best-performing ML model capable accurately predicting arbitrary compositions within 7-element system. efficient along with recently developed estimate strength HEAs used explore strength–SFE Pareto front, new-candidate alloys particularly interesting behavior.