Neural network potential molecular dynamics simulations of (La,Ce,Pr,Nd)0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)0.05F2.95
Yoyo Hinuma

TL;DR
This study uses neural network potential molecular dynamics to analyze fluoride ion conduction in Tysonite-structured fluorides, revealing temperature-dependent diffusion mechanisms and the influence of dopant cation size on F- mobility.
Contribution
The paper introduces NNP-MD simulations to investigate F- conduction in Tysonite fluorides, providing insights into diffusion behavior and mechanisms below and above 500 K.
Findings
F diffusion coefficient is higher with similar-sized divalent and trivalent cations.
F diffusion behavior changes around 500 K, with only F1 sites active below this temperature.
CeF3 exhibits higher diffusion despite larger lattice parameters, possibly due to shallower potential wells.
Abstract
Tysonite structure fluorides doped with divalent cations, represented by Ce0.95Ca0.05F2.95, are a class of good F- ion conductors together with fluorite-structured compounds. Computational understanding of the F- conduction process is difficult because of the complicated interactions between three symmetrically distinct F sites and the experimentally observed change in the F diffusion mechanism slightly above room temperature, effectively making first principles molecular dynamics (FP-MD) simulations, which are often conducted well above the transition temperature, useless when analyzing behavior below the transition point. Neural network potential (NNP) MD simulations showed that the F diffusion coefficient is higher when the divalent dopant cation size is similar to the trivalent cation size. The diffusion behavior of F in different sites changes at roughly 500 K in Ce0.95Ca0.05F2.95…
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