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

dc.contributor.author Khakurel, Hrishabh
dc.contributor.author Taufique, M. F. N.
dc.contributor.author Roy, Ankit
dc.contributor.author Balasubramanian, Ganesh
dc.contributor.author Ouyang, Gaoyuan
dc.contributor.author Cui, Jun
dc.contributor.author Johnson, Duane
dc.contributor.author Devanathan, Ram
dc.contributor.department Materials Science and Engineering
dc.contributor.department Chemical and Biological Engineering
dc.contributor.department Ames Laboratory
dc.contributor.department Physics and Astronomy
dc.date.accessioned 2022-03-28T19:44:27Z
dc.date.available 2022-03-28T19:44:27Z
dc.date.issued 2021-08-25
dc.description.abstract 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.
dc.description.comments This article is published as Khakurel, Hrishabh, M. F. N. Taufique, Ankit Roy, Ganesh Balasubramanian, Gaoyuan Ouyang, Jun Cui, Duane D. Johnson, and Ram Devanathan. "Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys." Scientific Reports 11, no. 1 (2021): 1-10. DOI: 10.1038/s41598-021-96507-0. Copyright 2021 Battelle Memorial Institute. Attribution 4.0 International (CC BY 4.0). Posted with permission.
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/Qr9meemr
dc.language.iso en
dc.publisher Springer Nature
dc.source.uri https://doi.org/10.1038/s41598-021-96507-0 *
dc.title Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys
dc.type Article
dspace.entity.type Publication
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