Materials informatics for the screening of multi-principal elements and high-entropy alloys
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 13, 2019
- Source ID
- 10.1038/s41467-019-10533-1
Entities
People
- Abhishek Roy
- Christopher J. Marvel
- G. Balasubramanian
- Helen M. Chan
- J. M. Rickman
- Joshua A. Smeltzer
- M. P. Harmer
Organizations
- Office of Naval Research Global