Identifying Novel Lithium Superionic Conductors Using a High-Throughput Screening Model Based on Structural Parameters

As the key component in solid-state batteries, Li superionic conductors ought to exhibit high ionic conductivities (>10(-4) S cm(-1)) at room temperature (sigma(RT)). However, identifying such materials is a grand challenge due to the limited number of known candidates and the difficulty of predicting sigma(RT) with both efficiency and accuracy. Herein, a high-throughput screening model is developed that requires only two easily accessible parameters: the diameter of Li-ion diffusion path (D-path) and the dimension of Li-ion network (D-Li). This model successfully identifies Li superionic conductors from 132 experimentally available Li-ion conductors. Using this approach, 13 new candidates are screened out of the 21 686 Li-containing materials from the Materials Project, and their Li superionic conductivity is confirmed by first-principle molecular dynamics simulations. Notably, two N-containing materials (i.e., Li6.5Ta0.5W0.5N4 and Li6.5Nb0.5W0.5N4) are identified, enriching the rare N-based Li superionic conductor family, while Li2Mo3S4 achieves the highest conductivity of 6.24 x 10(-2) S cm(-1) due to its unique structure of interconnected Mo6O8 clusters, providing a robust and optimal diffusion path. Li6.5Ta0.5W0.5N4, Li6.5Nb0.5W0.5N4, and Li7PSe6 have been identified as promising solid-state electrolytes for use at the anode interface for the solid-state Li-ion batteries, while Li10X(PS6)(2) (X = Si, Ge, or Sn), Li2Mn0.75Ta0.5Sn0.5S4, and Li2Zn0.5TaS4 are suitable for the cathode interface. This work not only proposes a highly effective and accurate screening model for exploring Li superionic conductors but also provides several new frameworks for designing systems with ultrahigh sigma(RT) values.