[1E04-06] Properties of Fuel and Concrete

11:00 AM - 11:15 AM JST(2:00 AM - 2:15 AM UTC)

[1E04] Evaluation of Thermal Conductivity of (Ca,Sr)F₂ using Machine-Learning Molecular Dynamics

*Hiroki Nakamura¹, Keita Kobayashi¹, Masahiko Okumura¹, Mitsuhiro Itakura¹, Masahiko Machida¹, Masashi Watanabe¹, Kato Masato² (1. JAEA, 2. Inspection Development)

Keywords:

First-principles Calculations,Machine-Learning Molecular Dynamics,CaF2,SrF2,Thermal Conductivity

When measuring the thermal properties of MOX fuel materials, experiments in the high-temperature region near the melting point are challenging. Therefore, a solid solution of calcium fluoride and strontium fluoride, (Ca,Sr)F₂, which has a lower melting point and the same crystal structure, is often used as a substitute to evaluate high-temperature properties. In this study, we evaluated the thermal conductivity of (Ca,Sr)F₂ using machine-learning molecular dynamics trained on first-principles calculations. The thermal conductivity was calculated from the heat flux autocorrelation function based on the Green–Kubo formalism. While the thermal conductivity of solid solutions is observed to be lower than that of pure materials such as CaF₂ and SrF₂, this trend was successfully reproduced in our calculations. These results suggest that the present method can be applied to the evaluation of the thermal conductivity of MOX fuel.