About this Abstract |
| Meeting |
TMS Specialty Congress 2026
|
| Symposium
|
4th World Congress on High Entropy Alloys (HEA 2026)
|
| Presentation Title |
A High-Throughput Framework for HEA Design & Applications to Metalloid-Containing Systems
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| Author(s) |
Marshall Donald Allen, Michael A. Miller, Jianliang Lin, Anton Zahradnik, Mirella Vargas, William Watson, John Macha |
| On-Site Speaker (Planned) |
Marshall Donald Allen |
| Abstract Scope |
High-throughput (HT), machine learning-assisted methodologies are accelerating the discovery and optimization timeline for novel materials. This work integrates computational modeling, machine learning predictions, and HT experimental techniques into an iterative alloy discovery framework. Using HT synthesis of graded thin-film compositional libraries and subsequent property characterization, we develop unique insights into several HEA systems. In particular, we investigate the use of metalloids such as boron and silicon for developing single phase HEAs with exceptional high-temperature specific strength. Overall, our framework enables efficient exploration of complex HEA composition spaces, generating insights into phase stability, microstructure evolution, and mechanical performance. Here, we will highlight the development of our computational-experimental framework, the integration of machine learning predictions with experimental data, and our discoveries while incorporating metalloids in pursuit of high temperature HEAs. By tailoring our computational and experimental strategies, we accelerate the discovery and refinement of HEAs optimized for high performance in extreme environments. |
| Proceedings Inclusion? |
Undecided |