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Machine-learning molecular dynamics study of thermal properties of CaF$$_2$$

Nakamura, Hiroki; Machida, Masahiko

It is not easy to measure high-temperature properties of actinide dioxides, which are the main component of nuclear fuels, due to their high melting points. CaF$$_2$$ is alternative materials of actinide dioxides since it has the same crystal structure and a lower melting point. In this paper, we perform machine-learning molecular dynamics with the Behler-Parrinello neural-network potential which is optimized by first-principles calculation results. We confirm the Bredig transition peak in the heat capacity and coefficient of thermal expansion in calculated results. We also discuss the availability of the present neural-network potential to analyze the high-temperature properties of CaF$$_2$$.

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