Towards stacking fault energy engineering in FCC high entropy alloys
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. 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.
- Authors:
-
- 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)
- Publication Date:
- Research Org.:
- Ames Lab., Ames, IA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; National Science Foundation (NSF)
- OSTI Identifier:
- 1833537
- Report Number(s):
- IS-J-10,663
Journal ID: ISSN 1359-6454
- Grant/Contract Number:
- AC02-07CH11358; DGE-1545403; DEMS-1663130; DMREF-1729350
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Acta Materialia
- Additional Journal Information:
- Journal Volume: 224; Journal ID: ISSN 1359-6454
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; high entropy alloys; stacking fault energy; machine learning; alloy design
Citation Formats
Khan, Tasneem Z., Kirk, Tanner, Vazquez, Guillermo, Singh, Prashant, Smirnov, A. V., Johnson, Duane D., Youssef, Khaled, and Arróyave, Raymundo. Towards stacking fault energy engineering in FCC high entropy alloys. United States: N. p., 2021.
Web. doi:10.1016/j.actamat.2021.117472.
Khan, Tasneem Z., Kirk, Tanner, Vazquez, Guillermo, Singh, Prashant, Smirnov, A. V., Johnson, Duane D., Youssef, Khaled, & Arróyave, Raymundo. Towards stacking fault energy engineering in FCC high entropy alloys. United States. https://doi.org/10.1016/j.actamat.2021.117472
Khan, Tasneem Z., Kirk, Tanner, Vazquez, Guillermo, Singh, Prashant, Smirnov, A. V., Johnson, Duane D., Youssef, Khaled, and Arróyave, Raymundo. Thu .
"Towards stacking fault energy engineering in FCC high entropy alloys". United States. https://doi.org/10.1016/j.actamat.2021.117472. https://www.osti.gov/servlets/purl/1833537.
@article{osti_1833537,
title = {Towards stacking fault energy engineering in FCC high entropy alloys},
author = {Khan, Tasneem Z. and Kirk, Tanner and Vazquez, Guillermo and Singh, Prashant and Smirnov, A. V. and Johnson, Duane D. and Youssef, Khaled and Arróyave, Raymundo},
abstractNote = {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.},
doi = {10.1016/j.actamat.2021.117472},
journal = {Acta Materialia},
number = ,
volume = 224,
place = {United States},
year = {Thu Nov 18 00:00:00 EST 2021},
month = {Thu Nov 18 00:00:00 EST 2021}
}
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