|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Advances in Multi-Principal Elements Alloys X
||Data-Driven Discovery of High-entropy Alloys
||George Kim, Chanho Lee, Peter Liaw, Wei Chen
|On-Site Speaker (Planned)
The material-design strategy of combining multiple elements in near-equimolar ratios has spearheaded the emergence of high-entropy alloys (HEAs), an exciting class of materials with exceptional engineering properties. While HEAs cover a broad compositional space, the understanding of elemental combinations and their effects is still limited. We employed high-throughput first-principles calculations, machine learning, and association rule mining to uncover synergies of elements for HEA design. These computational results will be discussed with experimental validations in the space of refractory high-entropy alloys.
||High-Entropy Alloys, Modeling and Simulation, Machine Learning