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AuthorTasneem Z., Khan
AuthorKirk, Tanner
AuthorVazquez, Guillermo
AuthorSingh, Prashant
AuthorSmirnov, A.V.
AuthorJohnson, Duane D.
AuthorYoussef, Khaled
AuthorArróyave, Raymundo
Available date2022-05-11T04:36:59Z
Publication Date2022-02-01
Publication NameActa Materialia
Identifierhttp://dx.doi.org/10.1016/j.actamat.2021.117472
CitationKhan, 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.
ISSN13596454
URIhttps://www.sciencedirect.com/science/article/pii/S135964542100851X
URIhttp://hdl.handle.net/10576/30807
AbstractStacking 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.
Languageen
PublisherElsevier
SubjectHigh entropy alloys
Stacking fault energy
Machine learning
Alloy design
TitleTowards stacking fault energy engineering in FCC high entropy alloys
TypeArticle
Volume Number224
dc.accessType Open Access


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