Computational screening of Li and Na fast ion conductors using high-throughput bond-valence calculations and machine-learning analysis

Javier Carrasco
CIC Energigune,

Keywords: battery materials, ionic conductivity, solid electrolyte, Na-ion batteries, bond-valence


Progress in electrochemical energy storage technology demands new and improved materials with high ionic conductivities. Li- and Na-based compounds have high priority in this regard owing to their importance for Li- and Na-ion batteries. In this work we implemented a high-throughput exploration of the chemical space for such compounds using bond-valence calculations. Our results reveal that there are significantly fewer Na-based conductors with low migration energies as compared to Li-based ones. This is traced to the fact that, in contrast to Li, the low diffusion barriers hinge on unusual values of some structural properties. To rationalize these findings, crystal structures were characterized through descriptors derived from bond-valence theory, graph percolation and geometric analysis. And a machine-learning analysis revealed that the ion migration energy is mainly determined by the global bottleneck for ion migration, by the coordination number of the cation and by the volume fraction of the mobile species. This workflow was implemented in the open-source Crystallographic Fortran Modules Library (CrysFML) and the program BondStr.