| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Meeting Materials Challenges for the Future of Fusion Energy
|
| Presentation Title |
Effect of Transmutation Products on Point Defect Energies in Tungsten From First-Principles and Machine Learning |
| Author(s) |
Anus Manzoor, Spencer Thomas, Jason R. Trelewicz |
| On-Site Speaker (Planned) |
Anus Manzoor |
| Abstract Scope |
Tungsten (W) is the leading candidate for plasma-facing armor in future fusion reactors. Transmutation products such as Re and Os are generated in W under the 14 MeV peaked fusion neutron spectrum. These impurities interact with radiation-induced point defects and significantly affect the performance of the material. In this study, point defect energies (formation, binding, and migration energies) have been calculated in W under different local chemical environments focusing on the introduction of the transmutation products Re and Os . Density functional theory (DFT) was employed as the primary simulation method; however, to reduce the computational burden of DFT calculations, machine learning (ML) models have been developed and implemented in the computational workflow. We show that our ML-DFT framework accurately captures the extensive energy landscape of point defects in the presence of transmutation impurities in W. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Biomaterials, |