| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Meeting Materials Challenges for the Future of Fusion Energy
|
| Presentation Title |
Development of Machine Learning Potential to Study Tritium Behavior in V-Alloy Blanket with Liquid Lithium Breeder |
| Author(s) |
Krishna Pitike, Prashanth Srinivasan, Duc Nguyen, Mark Gilbert, Wahyu Setyawan |
| On-Site Speaker (Planned) |
Krishna Pitike |
| Abstract Scope |
V-Cr-Ti alloys show great promise as structural materials for blankets in future fusion reactors due to their outstanding resistance to neutron irradiation, excellent mechanical properties at high temperatures, and good compatibility with liquid lithium. The use of liquid Li as a tritium breeder is essential for self-sustaining the tritium used as fuel in the fusion power plants. However, tritium transport through the interface between the liquid Li and the V-Cr-Ti alloys and retention in the alloys under high-temperature reactor conditions is not well understood. In this research, we have developed a neural network-based machine learning potential (MLP) for studying tritium behavior at liquid-Li/bcc-V interfaces, chosen as a model system. To accomplish this, the potential is trained using density functional theory data with atomic configurations relevant to fusion reactor operating temperatures. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Nuclear Materials |