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We present a high-throughput computational screening for fast lithium-ion conductors to identify promising materials for application in all solid-state electrolytes. Starting from more than 30 000 Li-containing experimental structures sourced from the Crystallography Open Database, Inorganic Crystal Structure Database and Materials Platform for Data Science, we perform highly automated calculations to identify electronic insulators. On these ∼1000 structures, we use molecular dynamics simulations to estimate Li-ion diffusivities using the pinball model, which describes the potential energy landscape of diffusing lithium with accuracy similar to density functional theory while being 200–500 times faster. Then we study the ∼60 most promising and previously unknown fast conductors using full first-principles molecular dynamics simulations at several temperatures to estimate their activation barriers. The results are discussed in detail for the 9 fastest conductors, including Li7NbO6, which shows a remarkable ionic conductivity of ∼5 mS cm−1 at room temperature. We further present the entire screening protocol, including the workflows where the accuracy of the pinball model is improved self-consistently, necessary for automatically running the required calculations and analysing their results.
Michael Graetzel, Ladislav Kavan