| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
High Entropy Materials: Concentrated Solid Solution, Intermetallics, Ceramics, Functional Materials and Beyond
|
| Presentation Title |
Using alloy phase diagrams to predict formation of high-entropy alloy phases |
| Author(s) |
Jie Qi, Mark Wischhusen, Samuel Inman, John R. Scully, Sean R. Agnew, S. Joseph Poon |
| On-Site Speaker (Planned) |
Jie Qi |
| Abstract Scope |
A refined method for High Entropy Alloy (HEA) phase prediction is essential but challenging in accelerating the discovery of high-performance HEAs. To date, only a very limited portion of the vast HEA compositional space has been explored. In this talk, we will present a HEA phase prediction method that mines useful information from binary phase diagrams, which leads to the use of unconventional machine learning (ML) model features formulated based on the phase field regions appropriately defined by composition and temperature. The success rate of this method is near 85 % in predicting the formation of single FCC, BCC, and HCP phases as well as composite phases that contain a limited number of intermetallic phases. The method has been experimentally validated. Further development of the ML model enables prediction of a broader range of HEA composites. |