[1E05-09] Mechanical Learning, AI Aplication

[1E06] Evaluation of high-temperature properties of (Ca,Sr)F₂ with machine-learning molecular dynamic

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

Keywords:CaF2, SrF2, Machine-learning molecular dynamics, Bredig transition

It is difficult to measure the thermal properties of nuclear fuel materials such as MOX fuel materials, at high temperature just below their melting points. Solid solution of calcium fluoride and strontium fluoride, (Ca,Sr)F₂, is sometimes employed as alternative MOX fuel materials. In this paper, we evaluate the thermal properties of (Ca,Sr)F₂ using machine-learning molecular dynamics trained by first-principles calculations. In particular, we investigate high-temperature properties such as Bredig transition. We also discuss application of the present method to MOX fuel materials.