| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials and Chemistry for Molten Salt Systems
|
| Presentation Title |
Structure-properties Relations in Molten FLiBe from Molecular-dynamics Simulations Based on Machine-learned Potentials |
| Author(s) |
Andrea Hwang, Nicholas Winner, Yury Lysogorskiy, Anton Bochkarev, Siamak Attarian, Sean Fayfar, Boris Khaykovich, Dane Morgan, Izabela Szlufarska, Ralf Drautz, Raluca Scarlat, Mark Asta |
| On-Site Speaker (Planned) |
Andrea Hwang |
| Abstract Scope |
Interest in 2LiF-BeF2 (FLiBe) molten salts for Generation IV reactor applications has motivated research into their thermophysical properties, and the ways they are affected by temperature and salt chemistry. Molecular dynamics (MD) simulations have been widely employed in this context, to derive properties that are otherwise challenging to measure. Recent neutron and x-ray scattering measurements for molten FLiBe provide a new opportunity for validating predictions from such MD simulations. In the current work, we undertake comparisons of these measurements with MD predictions derived from recently developed machine-learned (ML) potential models for FLiBe, including new potentials based on the Atomic Cluster Expansion formalism. We focus on both short and medium range order associated with BeF4 associate and oligomer formation. This information is correlated with properties including diffusivity, viscosity, and thermal conductivity, to derive understanding of the ways in which they influence the static and dynamic characteristics of such local order. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Temperature Materials, Modeling and Simulation, Machine Learning |