[2E01-07] Evaluation of physical properties of nuclear fuel

[2E02] Machine-learning molecular dynamics of simulated fuel materials CaF₂

*Hiroki Nakamura¹, Masahiko Machida¹, Masato Kato¹ (1. JAEA)

Keywords:fluorite, nuclear fuel materials, machine-learning molecular dynamics

It is difficult to measure the physical properties of fuel materials, such as uranium dioxide, at a high temperature near their melting point. In such cases, fluorite is often adopted as simulated fuel materials, because of its lower melting point than that of uranium dioxide. In this presentation, we evaluate the high-temperature behavior of fluorite using machine-learning molecular dynamics whose training data are obtained by first-principles calculations. Comparing calculated results with experimental data, we confirm the reliability and effectiveness of this method and discuss the possibility of application to fuel materials.