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

Author

Listed:
  • J. M. Rickman

    (Lehigh University
    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)

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.

Suggested Citation

  • J. M. Rickman & H. M. Chan & M. P. Harmer & J. A. Smeltzer & C. J. Marvel & A. Roy & G. Balasubramanian, 2019. "Materials informatics for the screening of multi-principal elements and high-entropy alloys," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10533-1
    DOI: 10.1038/s41467-019-10533-1
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