Materials informatics for the screening of multi-principal elements and high-entropy alloys

J. M. Rickman (), H. M. Chan, M. P. Harmer, J. A. Smeltzer, C. J. Marvel, A. Roy and G. Balasubramanian
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J. M. Rickman: Lehigh University
H. M. Chan: Lehigh University
M. P. Harmer: Lehigh University
J. A. Smeltzer: Lehigh University
C. J. Marvel: Lehigh University
A. Roy: Lehigh University
G. Balasubramanian: Lehigh University

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract The field of multi-principal element or (single-phase) high-entropy (HE) alloys has recently seen exponential growth as these systems represent a paradigm shift in alloy development, in some cases exhibiting unexpected structures and superior mechanical properties. However, the identification of promising HE alloys presents a daunting challenge given the associated vastness of the chemistry/composition space. We describe here a supervised learning strategy for the efficient screening of HE alloys that combines two complementary tools, namely: (1) a multiple regression analysis and its generalization, a canonical-correlation analysis (CCA) and (2) a genetic algorithm (GA) with a CCA-inspired fitness function. These tools permit the identification of promising multi-principal element alloys. We implement this procedure using a database for which mechanical property information exists and highlight new alloys having high hardnesses. Our methodology is validated by comparing predicted hardnesses with alloys fabricated by arc-melting, identifying alloys having very high measured hardnesses.

Date: 2019
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DOI: 10.1038/s41467-019-10533-1

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