Capturing the interactions in the BaSnF4 ionic conductor: comparison between a machine-learning potential and a polarizable force field

18 September 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


BaSnF4 is a prospective solid state electrolyte for fluoride ion batteries. However, the diffusion mechanism of the fluoride ions remains difficult to study, both in experiments and in simulations. In principle, ab initio molecular dynamics could allow to fill this gap, but this method remains very costly from the computational point of view. Using machine learning potentials is a promising method that can potentially address the the accuracy issues of classical empirical potentials while maintaining high efficiency. In this work, we fitted a dipole polarizable ion model and trained machine learning potential for BaSnF4 and made comprehensive comparisons on the ease of training, accuracy and efficiency. We also compared the results with the case of a simpler ionic system (NaF). We show that contrarily to the latter, for BaSnF4 the machine learning potential offers much higher versatility. The current work lays foundations for the investigation of fluoride ion mobility in BaSnF4 and provides insight on the choice of methods for atomistic simulations.


fluoride ion conductor
polarizable force field
machine learning potential

Supplementary materials

Supplementary Table
Comparison of various DFT functionals, force field parameters


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