Materials Informatics for the Screening of Multi-Principal Elements and High-Entropy Alloys
Journal Article - Open Access
Lehigh University Bethlehem United States
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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 chemistrycomposition 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.
- Properties of Metals and Alloys
- Statistics and Probability