About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Modeling, AI Applications, and Method Development in Nuclear Materials
|
| Presentation Title |
Hydrogen Diffusivity in Nb-based High Entropy Alloys for Fusion Reactors from DFT, kMC and ML |
| Author(s) |
Oriyomi Opetubo, Dale Hitchcock, Dilpuneet Aidhy |
| On-Site Speaker (Planned) |
Oriyomi Opetubo |
| Abstract Scope |
Hydrogen recovery is critical for deuterium-tritium fuel cycles in nuclear fusion reactors, where fast H permeation is desired. Nb-based high-entropy alloys (HEAs) are promising candidates for hydrogen separation due to their exceptional permeability. The multi-element nature of HEAs provides opportunities to mitigate hydrogen embrittlement while maintaining high H permeability. In this work, we investigate equimolar Nb-alloys composed of Groups 4, 5 and 6 alloying elements to understand the binding and migration energies, attempt frequencies, leading to predictions of H diffusivities. We integrate machine learning (ML) models to predict all of the aforementioned quantities in varying NN environments and feed to kinetic Monte Carlo (KMC) simulations to calculate diffusivity. We elucidate that an equimolar Nb-Zr-Hf (and Nb-Zr-Hf-Mo-Ti) demonstrates diffusion coefficient of 7.51 x 10-9 m2/s at 400oC, then Pd-23Ag ( ~1.5 x 10-9 – 3.5x10-9 m2/s). This approach enables prediction of Nb-based HEAs with minimal trapping, high diffusivity, and superior permeability |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, High-Entropy Alloys, Nuclear Materials |