High-throughput screening of sulfur-based silver superionic conductors

Superionic conductors (SICs) are attracting significant research attention for their potential applications in all-solid-state batteries. A key challenge in developing such SICs is the rational design of sulfide structural frameworks that enable rapid ion transport. Compared with the conventional trial-and-error approach, theory-driven computational methods offer powerful tools for accelerating materials discovery. In this study, we use the largest cavity diameter (LCD) as a descriptor and conduct high-throughput screening of sulfur-based silver compounds using the Materials Project (MP) database to identify promising superionic-state materials. Machine-learning force-field (MLFF)-accelerated molecular-dynamics (MD) simulations are performed on the selected structures, yielding seven candidate materials, including several previously reported systems. Furthermore, the superionic-state transition temperatures of these compounds are 300 K or higher. Notably, the LCD value is shown to be inversely proportional to the superionic-state transition temperature. This work establishes a quantitative structure–property relationship governing superionic-state transitions and provides a framework for future computational investigations of superionic materials.