Unraveling Li-ion transport mechanisms in high-entropy anion-disordered argyrodites via machine-learned interatomic potentials

Abstract

Lithium argyrodite sulfide solid electrolytes are widely used in all-solid-state batteries owing to their high ionic conductivity. Recently, high-entropy argyrodites formed by anion disorder in Li₆PS₅Cl have emerged as promising superionic conductors. However, the details of the Li-ion conduction mechanism in high-entropy argyrodites have yet to be fully elucidated. In this study, the Li-ion conduction mechanism is systematically investigated through first-principles calculations and molecular dynamics simulations using machine-learned interatomic potentials (MLIPs). The calculations indicate that high-entropy Li₆PS₅Cl argyrodites improve site energy uniformity and facilitate inter-cage jumps, significantly enhancing Li-ion conductivity. Ionic conductivity was further improved with increased disorder in Cl-rich argyrodites, but a critical threshold was observed with the addition of Cl. By leveraging MLIPs, a detailed analysis of the conduction mechanism was efficiently conducted, and a systematic investigation of ionic conductivity through entropy variation was performed. These findings highlight the reliability and effectiveness of MLIPs in facilitating the design and analysis of novel high-entropy superionic argyrodites.