Towards stacking fault energy engineering in FCC high entropy alloys

• Published in: Acta materialia Vol. 224; p. 117472
• Main Authors: Khan, Tasneem Z., Kirk, Tanner, Vazquez, Guillermo, Singh, Prashant, Smirnov, A.V., Johnson, Duane D., Youssef, Khaled, Arróyave, Raymundo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2022
• Subjects: Alloy design; High entropy alloys; Machine learning; Stacking fault energy; High entropy alloys; Stacking fault energy; Alloy design; Machine learning
• ISSN: 1359-6454; 1873-2453
• DOI: 10.1016/j.actamat.2021.117472

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. [Display omitted]