Data‐Driven Materials Exploration for Li‐Ion Conductive Ceramics by Exhaustive and Informatics‐Aided Computations

Abstract

Abstract Interest in all‐solid‐state Li‐ion batteries (LIBs) using non‐flammable Li‐conducting ceramics as solid electrolytes has increased, as safe and robust batteries are urgently desired as power sources for (hybrid) electric vehicles. However, the low Li‐ion conductivities of ceramics have hindered all‐solid‐state LIB commercialization; many researchers have attempted to develop fast Li‐ion conductors. We introduce two efficient high‐throughput computational approaches for materials exploration: (i) exhaustive search and (ii) informatics‐aided prediction. For demonstration, ∼400 Li‐ and Zn‐containing oxide (Li−Zn−X−O) compounds of varied crystal structures are extracted from Materials Project datasets. We calculate the migration energies for Li‐ion conduction and the phase stabilities (decomposition energies) of these materials by simulation and apply Bayesian optimization to determine the material with the highest ionic conductivity. The results show much greater efficiency than a random search algorithm.