| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials and Chemistry for Molten Salt Systems
|
| Presentation Title |
The Complex Structure of Molten 2LiF-BeF2 Using Neutron Scattering, X-ray Scattering, and Neural Network Molecular Dynamics |
| Author(s) |
Sean Fayfar, Rajni Chahal, Haley Williams, D. Nathanael Gardner, Guiqiu Zheng, David Sprouster, Joerg Neuefeind, Dan Olds, Andrea Hwang, Joanna Mcfarlane, Ryan C. Gallagher, Mark Asta, Stephen Lam, Raluca O. Scarlat, Boris Khaykovich |
| On-Site Speaker (Planned) |
Sean Fayfar |
| Abstract Scope |
The use of molten salts for use in energy applications such as a coolant fluid in new advanced nuclear reactors benefits from the characterization of their molecular structure to guide molecular dynamics simulations for predictive modeling. These molten salts benefit from improved thermo-physical characteristics that lead to reactors with an improve heat-to-efficiency ratio and passive safety systems while operating under atmospheric pressures. A prominent candidate salt, 2LiF-BeF2 (FLiBe), is the focus of this study. We performed total scattering measurements of molten FLiBe using both neutron and X-rays along with ab-initio and neural network simulations to uncover the local structure of the liquid under operating temperatures. We found agreement in the predicted molten structure with experimental measurements and the formation of intermediate-range order in the form of BeF42- oligomers that were only feasible with the longer length scales achieved with machine learning. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Nuclear Materials, Characterization, Machine Learning |