Observing nucleation and crystallization of rock salt LiF from molten state through molecular dynamics simulations with refined machine-learned force field

Lithium fluoride is a critical component for stabilizing lithium metal anodes and high-voltage cathodes toward the next-generation high-energy-density lithium batteries. A recent modeling study reported the formation of wurtzite LiF below ∼550 K (Hu et al., J. Am. Chem. Soc. 2023, 145, 1327-1333), in contrast to the experimental observation of rock salt LiF under ambient conditions. To address this discrepancy, we employ molecular dynamics simulations with a refined machine-learned force field (MLFF) and demonstrate the nucleation and crystallization of rock salt LiF from the molten phase at temperatures below ∼800 K. The rock salt phase remains stable in LiF nanoparticles. Complementary density functional theory calculations show that dispersion interactions are essential for correctly predicting the thermodynamic stability of rock salt LiF over the wurtzite phase on top of the commonly used PBE functional. Furthermore, we show that inclusion of virial stresses, alongside energies and forces, in the training of MLFFs is crucial for capturing phase nucleation and crystallization of rock salt LiF under the isothermal-isobaric ensemble. These findings underscore the critical role of dispersion interactions in atomistic simulations of battery materials, where such effects are often non-negligible, and highlight the necessity of incorporating virial stresses during the training of MLFFs to enable accurate modeling of solid-state systems.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2025-06-20
DOI
https://doi.org/10.1063/5.0276535
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.04

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article

Observing nucleation and crystallization of rock salt LiF from molten state through molecular dynamics simulations with refined machine-learned force field

Boyuan Xu, Shenzhen Xu, Liyi Bai, Qisheng Wu
2 citations
The Journal of Chemical Physics
Machine Learning in Materials Science
1.04
article

Observing nucleation and crystallization of rock salt LiF from molten state through molecular dynamics simulations with refined machine-learned force field

Boyuan Xu, Shenzhen Xu, Liyi Bai, Qisheng Wu
article en
2 citations

Abstract

Lithium fluoride is a critical component for stabilizing lithium metal anodes and high-voltage cathodes toward the next-generation high-energy-density lithium batteries. A recent modeling study reported the formation of wurtzite LiF below ∼550 K (Hu et al., J. Am. Chem. Soc. 2023, 145, 1327-1333), in contrast to the experimental observation of rock salt LiF under ambient conditions. To address this discrepancy, we employ molecular dynamics simulations with a refined machine-learned force field (MLFF) and demonstrate the nucleation and crystallization of rock salt LiF from the molten phase at temperatures below ∼800 K. The rock salt phase remains stable in LiF nanoparticles. Complementary density functional theory calculations show that dispersion interactions are essential for correctly predicting the thermodynamic stability of rock salt LiF over the wurtzite phase on top of the commonly used PBE functional. Furthermore, we show that inclusion of virial stresses, alongside energies and forces, in the training of MLFFs is crucial for capturing phase nucleation and crystallization of rock salt LiF under the isothermal-isobaric ensemble. These findings underscore the critical role of dispersion interactions in atomistic simulations of battery materials, where such effects are often non-negligible, and highlight the necessity of incorporating virial stresses during the training of MLFFs to enable accurate modeling of solid-state systems.

The Journal of Chemical PhysicsVol. 162(23)
Peking University (CN), Division of Materials Science and Engineering (AU)
Ministry of Industry and Information Technology of the People's Republic of China
Affordable and clean energy
Openalex Percentile: Top 24%
Machine Learning in Materials Science
1.04
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