|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Advances in Multi-Principal Elements Alloys X
||Machine-Learning and High-Throughput Studies for High-Entropy Materials: Building Beyond & Above
||E-Wen Huang, Sudhanshu Shekhar Singh, Wen-Jay Lee, Chih-Yu Lee, Hsu-Hsuan Chin, Poresh Kumar, Peter K. Liaw, Bi-Hsuan Lin
|On-Site Speaker (Planned)
The combination of multiple-principal element materials, known as high-entropy materials (HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is impossible to afford a holistic approach to explore each possibility. With the advance of the materials genome initiative and characterization technology, a high-throughput (HT) approach is more reasonable, especially to identify the specified functions for the new HEMs development. There are three major components for the HT approach, which are the computational tools, experimental tools, and digital data. This talk will review both the materials informatics and experimental approaches for the HT methods. The ML-developed HEMs together with ML-created other materials are positioned in this talk.
||High-Entropy Alloys, Machine Learning, ICME