Efficient automatic screening for Li ion conductive inorganic oxides with bond valence pathway models and percolation algorithm

Abstract

Fast ion conductive solid oxide electrolytes are urgently needed because of the development of batteries, fuel cells, and sensors. Ab initio density functional theory can predict ionic conductivities with high accuracy, although it often requires large computational resources and time. In this paper, we use empirical bond valence relations [Adams et al., Phys. Status Solidi A 208, 1746 (2011)] and a percolation algorithm for fast, efficient, fully automated evaluation of migration energies for Li ion conduction in 14 olivine-type LiMXO4 compounds. The results showed a high correlation coefficient with the ab initio density functional theory (DFT) approach, indicating that our method could be attractive for identifying fast ion conductors in databases of numerous candidates.