| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Meeting Materials Challenges for the Future of Fusion Energy
|
| Presentation Title |
Development of Machine Learned Interatomic Potentials for Modeling Transmutation Products in Fusion First Wall Materials |
| Author(s) |
Mary Alice Cusentino, Anus Manzoor, Yusheng Jin, Thomas Spencer, Krishna C. Pitike, Wahyu Setyawan, Rafi Ullah, Izabela Szlufarska, Jason R. Trelewicz, Jaime Marian |
| On-Site Speaker (Planned) |
Mary Alice Cusentino |
| Abstract Scope |
First wall materials within fusion reactors will be subject to high fluxes of 14 MeV neutrons which will introduce transmutation products into these materials. The transmutation products will detrimentally affect the material performance of the first wall materials, impacting properties like elastic constants, thermal conductivity, and defect interactions within these materials. Atomistic modeling techniques like molecular dynamics are well suited to study this phenomenon. However, interatomic potentials do not currently exist or are limited in their accuracy of key material properties for modeling transmutation properties. In this work, we will discuss the development of machine learned interatomic potentials to study the effect of transmutation products on the performance of first wall fusion materials. Considered materials will include W-Re-Os, Fe-Cr-Mn-W, and SiC-Mg. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Modeling and Simulation, Nuclear Materials |