Machine learning strategies for high-entropy alloys
Abstract
The study of high-entropy (HE) alloys has seen dramatic growth in recent years as, in some cases, these systems can exhibit exceptional properties, including enhanced oxidation resistance, superior mechanical properties, and desirable magnetic properties. The identification of promising HE alloys is, however, extremely challenging due to the extraordinarily large number of distinct systems that may be fabricated from the available palette of elements. For this reason, machine learning strategies have been employed to reduce the size of the associated chemistry/composition space. In this review, we outline several computational strategies that have led to the identification of useful alloys and discuss the relative merits and shortcomings of these approaches. We also present short tutorials illustrating the use of selected computational approaches to HE characterization and design.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 10, 2020
- Source ID
- 10.1063/5.0030367
Entities
People
- Christopher J. Marvel
- G. Balasubramanian
- Helen M. Chan
- Jeffrey Rickman
- M.-t. Burton
Organizations
- Lehigh University
- National Science Foundation
- Office of Naval Research Global