Towards stacking fault energy engineering in FCC high entropy alloys
Author | Tasneem Z., Khan |
Author | Kirk, Tanner |
Author | Vazquez, Guillermo |
Author | Singh, Prashant |
Author | Smirnov, A.V. |
Author | Johnson, Duane D. |
Author | Youssef, Khaled |
Author | Arróyave, Raymundo |
Available date | 2022-05-11T04:36:59Z |
Publication Date | 2022-02-01 |
Publication Name | Acta Materialia |
Identifier | http://dx.doi.org/10.1016/j.actamat.2021.117472 |
Citation | Khan, T. Z., Kirk, T., Vazquez, G., Singh, P., Smirnov, A. V., Johnson, D. D., ... & Arróyave, R. (2022). Towards stacking fault energy engineering in FCC high entropy alloys. Acta Materialia, 224, 117472. |
ISSN | 13596454 |
Abstract | 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. |
Language | en |
Publisher | Elsevier |
Subject | High entropy alloys Stacking fault energy Machine learning Alloy design |
Type | Article |
Volume Number | 224 |
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Materials Science & Technology [294 items ]