Temperature-Dependent Structural, Thermodynamic, and Phonon Properties of Lithium Fluoride from a Neuro-Evolution Machine-Learning Potential
DOI:
https://doi.org/10.15377/2409-5826.2025.12.7Keywords:
Lithium fluoride (LiF), Phonon anharmonicity, Neuro-evolution potential (NEP), Thermal conductivity and expansion, High-temperature molecular dynamics, Machine-learning interatomic potentialAbstract
Accurate prediction of structural and thermodynamic properties at finite temperature is a prerequisite for deploying ionic-crystal LiF in solid-state electrolytes and molten-salt coolants. To simultaneously boost predictive accuracy and accessible simulation scale, we have developed a neuro-evolution potential (NEP) machine-learning model for LiF. The potential exhibits outstanding fidelity and stability when describing high-temperature structures and thermodynamics. Force-field molecular dynamics driven by this NEP yields a thermal-expansion coefficient and lattice thermal conductivity in excellent agreement with experiments. LiF displays remarkable dynamical stability at elevated temperature, a conclusion corroborated by its elastic constants and phonon dispersion relations. A pronounced frequency resonance between Li and F motions appears at high temperature, underscoring strong phonon anharmonicity. This work delivers a rigorously validated and transferable potential for LiF, providing both a research framework and a data foundation for future investigations of its behaviour at electrolyte interfaces, in high-temperature solid solutions and under other extreme thermodynamic conditions.
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