About this Abstract |
| Meeting |
Materials in Nuclear Energy Systems (MiNES) 2025
|
| Symposium
|
Materials in Nuclear Energy Systems (MiNES) 2025
|
| Presentation Title |
Transport Properties of Chloride Molten Salts From Machine Learning Interatomic Potentials |
| Author(s) |
Benjamin W. Beeler, Gabe Walton, Gwen White, Alexander Bataller |
| On-Site Speaker (Planned) |
Benjamin W. Beeler |
| Abstract Scope |
Molten salts exhibit excellent thermal properties, leading to potential applications as a heat storage and transport medium. One application involves the utilization of molten salts as an advanced nuclear fuel and coolant. For the deployment of these advanced reactor systems, knowledge of thermophysical and transport properties is required for design and safety analyses. In this work, ab initio molecular dynamics simulations of molten salts are performed to parameterize machine learning interatomic potentials. Novel experimental data on thermophysical, structural, and vibrational properties are used to inform and refine the fitting processes. Subsequently, the generated interatomic potentials are applied to determine viscosity and thermal conductivity from classical molecular dynamics across a wide range of temperatures and compositions. This work demonstrates a novel pathway for machine learning-based interatomic potential development and determines key fundamental transport properties in composition-temperature phase spaces where experimental data does not exist. |
| Proceedings Inclusion? |
Undecided |