| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
High Entropy Alloys IX: Structures and Modeling
|
| Presentation Title |
Accelerated Exploration of Refractory Multi-principal Element Alloys by Machine Learning |
| Author(s) |
Carolina Frey, Christopher Borg, James Saal, Bryce Meredig, Daniel Miracle, Tresa Pollock |
| On-Site Speaker (Planned) |
Carolina Frey |
| Abstract Scope |
Refractory-based multi-principal element alloys (RMPEAs) are of interest for high temperature structural applications due to their high melting points but remain relatively underexplored compared to other MPEA categories due to processing challenges. Search strategies and experimental methods that reduce the number of needed experiments are necessary to more efficiently discover interesting materials. This presentation will discuss the use of random forest machine learning in concert with rapid processing and characterization techniques to guide sequential alloy design. An updated and publicly available database, which doubles the number of recorded compositions and mechanical properties of previously published MPEA databases, was developed for use as training data. A rapid solidification technique, splat quenching, was used to reduce segregation and the grain size of synthesized materials to evaluate a greater range of properties and allow for more rapid experimental exploration of interesting RMPEA compositions. New alloys with high strength will be discussed. |
| Proceedings Inclusion? |
Planned: TMS Journal: Metallurgical and Materials Transactions |
| Keywords |
High-Entropy Alloys, High-Temperature Materials, Machine Learning |