About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Materials Corrosion Behavior in Advanced Nuclear Reactor Environments
|
Presentation Title |
Studies of Complexation of Ni-Cr in Molten Salts Using Machine Learning Interatomic Potentials |
Author(s) |
Siamak Attarian, Dane Morgan, Izabela Szlufarska |
On-Site Speaker (Planned) |
Siamak Attarian |
Abstract Scope |
Corrosion of structural materials in molten salt reactors is a major concern. Impurities are the main driver of corrosion and studying their complexation in the salt is of interest. Machine learning interatomic potentials are powerful tools for accurate and long molecular dynamics (MD) simulations to study the energetics of molten salts containing impurities. Here we use the atomic cluster expansion method to fit potentials to FLiBe with Cr and Ni impurities, run long MD simulations, and study their complexation. Our findings suggest that there is a rather weak binding between CrF2 and NiF2 and the redox potential of NiF2 is lowered in the presence of CrF2 in FLiBe, making Ni dissolution more favorable in the presence of Cr compared to its dissolution in pure FLiBe. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Modeling and Simulation, Nuclear Materials |