| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Refractory Metals 2025
|
| Presentation Title |
Systematic exploration of refractory high entropy alloys using high-throughput techniques and machine learning |
| Author(s) |
Krzysztof Wieczerzak |
| On-Site Speaker (Planned) |
Krzysztof Wieczerzak |
| Abstract Scope |
The demand for alloys with exceptional high-temperature mechanical properties is crucial in industries such as aerospace and power generation. Refractory high entropy alloys (RHEAs) are promising candidates due to their high melting points and strength in extreme environments. This study focuses on exploring RHEAs within the Cr-Mo-Nb-Ta-V-W system. We synthesized a material library (MatLib) using physical vapor deposition on a silicon wafer, resulting in approximately 35,000 distinct alloys. The process was precisely calibrated to achieve equimolar composition and maximum configurational entropy at the center of the MatLib. Selected regions underwent chemical analysis using X-ray fluorescence and structural studies with X-ray diffraction, analyzed using Le Bail refinement to determine lattice parameters and crystallite size. High-throughput nanoindentation was employed to measure mechanical properties. The resulting data were used to train an artificial neural network to predict the properties of RHEAs beyond the tested compositional space. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, High-Temperature Materials, Machine Learning |