[1A04-06] High Temperature Thermal Property of Oxide Fuel

[1A05] Evaluation of high-temperature properties of CaF₂ with machine-learning molecular dynamics

*Hiroki Nakamura¹, Masahiko Machida¹ (1. JAEA)

Keywords:Machine-learning molecular dynamics, CaF2, First-priciples calculation

It is difficult to measure the thermal properties of nuclear fuel materials such as uranium dioxide, at high temperature just below their melting points. Calcium fluoride is sometimes employed as alternative nuclear fuel materials. In this paper, we evaluate the thermal properties of calcium fluoride using machine-learning molecular dynamics trained by first-principles calculations. In particular, we investigate high-temperature properties such as melting points and confirm the reliability and availability of the calculations comparing with experimental data. We also discuss application of the present method to nuclear materials such as uranium dioxide.