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

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Khakurel, Hrishabh
Taufique, M. F. N.
Roy, Ankit
Balasubramanian, Ganesh
Ouyang, Gaoyuan
Cui, Jun
Devanathan, Ram
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Iowa State University Digital Repository, Ames IA (United States)
Johnson, Duane
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Ouyang, Gaoyuan
Ames Laboratory Scientist II
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Materials Science and Engineering
Materials engineers create new materials and improve existing materials. Everything is limited by the materials that are used to produce it. Materials engineers understand the relationship between the properties of a material and its internal structure — from the macro level down to the atomic level. The better the materials, the better the end result — it’s as simple as that.
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Physics and Astronomy
Physics and astronomy are basic natural sciences which attempt to describe and provide an understanding of both our world and our universe. Physics serves as the underpinning of many different disciplines including the other natural sciences and technological areas.
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Materials Science and EngineeringChemical and Biological EngineeringAmes National LaboratoryPhysics and Astronomy
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.
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. DOE Contract Number(s): AC02-07CH11358; AC05-76RL01830.