Data-Driven Theoretical Design of Anion Cluster-Based Sodium Antiperovskite Superionic Conductors

Sodium antiperovskite materials (APs) are a promising class of solid-state electrolytes owing to their high structural tolerance and good formability. However, few APs have been synthesized experimentally, indicating the necessity of exploring potential chemical spaces with higher ionic conductivities. Herein, through a combined particle swarm optimization algorithm, high-throughput first-principles calculations, ab initio molecular dynamics, and long time-scale machine-learning molecular dynamics simulations, strategies based on site-exchanging and anion clusters are shown to simultaneously enhance the thermal stability and sodium diffusivity in the designed APs. Among these APs, the highest theoretical ionic conductivity of 39.05 mS/cm is achieved with Na3BrSO4 at room temperature due to the strong coupling of cluster rotation and sodium migration. We highlight that not only the rotation dynamics but also its coupling with Na diffusion contribute to the high ionic conductivity, as confirmed by the proposed local difference frequency center to evaluate the coupling degree. Our work designs promising site-exchanging APs and offers insights into the coupling between anion rotation and cation migration, which can effectively guide the design of superionic conductors with cluster rotation dynamics.