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

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2021-JohnsonDuane-MachineLearning.pdf (3.06 MB)

Date

2021-08-25

Authors

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

Publisher

Springer Nature

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Authors

Johnson, Duane

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Organizational Units

Materials Science and Engineering

Organizational Unit

Chemical and Biological Engineering

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Ames Laboratory

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Physics and Astronomy

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Department

Materials Science and EngineeringChemical and Biological EngineeringAmes LaboratoryPhysics and Astronomy

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.

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.