Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

Khakurel, Hrishabh; Taufique, M. F. N.; Roy, Ankit; Balasubramanian, Ganesh; Ouyang, Gaoyuan; Cui, Jun; Johnson, Duane D.; Devanathan, Ram

doi:10.1038/s41598-021-96507-0

Title: Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

Journal Article · 24 August 2021 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-021-96507-0 · OSTI ID:1819748

Khakurel, Hrishabh ^[1]; Taufique, M. F. N. ^[2]; Roy, Ankit ^[3]; Balasubramanian, Ganesh ^[3]; Ouyang, Gaoyuan ^[4]; Cui, Jun ^[5]; Johnson, Duane D. ^[5]; Devanathan, Ram ^[2]

Univ. of Texas, Arlington, TX (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Lehigh Univ., Bethlehem, PA (United States)
Ames Lab., Ames, IA (United States)
Ames Lab., Ames, IA (United States); Iowa State Univ., Ames, IA (United States)

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

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Research Organization:: Ames Laboratory (AMES), Ames, IA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: AC02-07CH11358; AC05-76RL01830

OSTI ID:: 1819748

Report Number(s):: IS-J--10,578; PNNL-SA--158789

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 11; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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