About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Environmental Degradation of Multiple Principal Component Materials
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| Presentation Title |
Accelerated Discovery of Oxidation-Resistant Refractory Compositionally Complex Alloys via High-Throughput Diffusion Multiples and Active Learning. |
| Author(s) |
Akhil Bejjipurapu, Sharmila Karumuri, Joseph C Flanagan, Saswat Mishra, Victoria Tucker, Alejandro H Strachan, Illias Bilionis, Kenneth H Sandhage, Michael Titus |
| On-Site Speaker (Planned) |
Akhil Bejjipurapu |
| Abstract Scope |
Refractory Compositionally Complex Alloys (RCCAs) can be promising alternatives to nickel-based superalloys for high-temperature applications. However, achieving satisfactory high-temperature oxidation and environmental resistance with RCCAs remains a challenge. The vast design space, encompassing nine refractory elements and aluminum, complicates efficient exploration. To address this challenge, we employ a Gaussian Process Regression active learning model integrated with low-fidelity, high-throughput diffusion-multiple experiments. This approach utilizes a combination of composition-based descriptors, atomistic descriptors related to oxide layering, and descriptors based on oxide properties. Oxidation experiments are conducted at 1000 °C, and the resulting external oxide scales are characterized using localized Raman spectroscopy to identify RCCA compositions that form protective alpha-alumina scales. By sampling the unique compositions across the interdiffusion zones in these diffusion multiples, we can screen numerous alloy compositions in a single experiment for their ability to develop external α-Al₂O₃ scales. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Temperature Materials, High-Entropy Alloys, |