ABSTRACT Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity (1 mS ), excellent chemical stability, mechanical softness, and facile synthesis routes via standard solid‐state reactions. These materials exhibit an ionic conductivity that is almost independent of the underlying chemistry, in stark contrast to what occurs in crystalline conductors. In this work, we employ machine learning interatomic potentials to construct large‐scale molecular dynamics trajectories encompassing hundreds of nanoseconds to obtain statistically converged transport properties. We find that the amorphous state consists of chain fragments of metal‐anion tetrahedra of various lengths. By analyzing the residence time of alkali cations migrating around tetrahedrally‐coordinated metals, we find that oxygen anions limit alkali diffusion. By computing the full Einstein expression of the ionic conductivity, we demonstrate that the alkali transference number of these materials is strongly influenced by distinct‐particles correlations, while alkali transport is dictated by uncorrelated self‐diffusion. By extending this analysis to chemical compositions , spanning different alkaline ( = Li, Na, K), metallic ( = Al, Ga, In), and halogen ( = Cl, Br, I) species, we clarify why the diffusion properties of these materials remain largely insensitive to variations in atomic isovalent chemistry.
Binci et al. (Thu,) studied this question.