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Machine Learned Feature Identification for Predicting Phase and Young's Modulus of Low-, Medium- and High-Entropy Alloys

33 Pages Posted: 5 Mar 2020 Publication Status: Under Review

See all articles by Ankit Roy

Ankit Roy

Lehigh University - Department of Mechanical Engineering and Mechanics

Tomas Babuska

Lehigh University - Department of Mechanical Engineering and Mechanics

Brandon Krick

Lehigh University - Department of Mechanical Engineering and Mechanics

Ganesh Balasubramanian

Lehigh University - Department of Mechanical Engineering and Mechanics

Abstract

Machine learning (ML) has emerged as a potential tool to rapidly accelerate the search for novel high entropy alloys (HEAs) due to its reasonably accurate property predictions. Here, we implement ML tools, to predict the crystallographic phase and Young's modulus of 26 Mo-Ta-Ti-W-Zr based HEAs. Our results, with experimental validation, reveal that mean melting point, electronegativity difference and the enthalpy of mixing are key features impacting the phase and Young's modulus of HEAs. Contrarily, entropy of mixing negligibly influences phase or the Young's modulus predictions, reigniting the issue of its actual impact on the phase and properties of HEAs.

Keywords: High-entropy alloys, machine learning, gradient boost algorithm, crystallographic phase, Young's modulus

Suggested Citation

Roy, Ankit and Babuska, Tomas and Krick, Brandon and Balasubramanian, Ganesh, Machine Learned Feature Identification for Predicting Phase and Young's Modulus of Low-, Medium- and High-Entropy Alloys. Available at SSRN: https://ssrn.com/abstract=3546579 or http://dx.doi.org/10.2139/ssrn.3546579

Ankit Roy (Contact Author)

Lehigh University - Department of Mechanical Engineering and Mechanics

United States

Tomas Babuska

Lehigh University - Department of Mechanical Engineering and Mechanics

United States

Brandon Krick

Lehigh University - Department of Mechanical Engineering and Mechanics

United States

Ganesh Balasubramanian

Lehigh University - Department of Mechanical Engineering and Mechanics ( email )

United States

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