Towards stacking fault energy engineering in FCC high entropy alloys

Khan, Tasneem Z.; Kirk, Tanner; Vazquez, Guillermo; Singh, Prashant; Smirnov, A. V.; Johnson, Duane D.; Youssef, Khaled; Arróyave, Raymundo

doi:10.1016/j.actamat.2021.117472

Towards stacking fault energy engineering in FCC high entropy alloys

Journal Article · Thu Nov 18 00:00:00 EST 2021 · Acta Materialia

DOI:https://doi.org/10.1016/j.actamat.2021.117472· OSTI ID:1833537

Khan, Tasneem Z. ^[1]; Kirk, Tanner ^[2]; Vazquez, Guillermo ^[2]; Singh, Prashant ^[3]; Smirnov, A. V. ^[4]; Johnson, Duane D. ^[4]; Youssef, Khaled ^[5]; Arróyave, Raymundo ^[2]

Univ. of California, Berkeley, CA (United States); Columbia Univ., New York, NY (United States)
Texas A & M Univ., College Station, TX (United States)
Texas A & M Univ., College Station, TX (United States); Ames Lab., and Iowa State Univ., Ames, IA (United States)
Ames Lab., and Iowa State Univ., Ames, IA (United States)
Qatar University, Doha (Qatar)

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. Finally, 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.

View Accepted Manuscript (DOE)

Research Organization:: Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-07CH11358

OSTI ID:: 1833537

Report Number(s):: IS-J--10,663

Journal Information:: Acta Materialia, Journal Name: Acta Materialia Vol. 224; ISSN 1359-6454

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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