Unveiling the Atomic-Scale Mechanism of High-Entropy Doping in LLZO Solid-State Electrolytes via Neuroevolution Potential-Guided Simulations and Experiments

Elucidating the atomic-scale ion transport mechanism in solid-state electrolytes is crucial for the development of all-solid-state batteries. As a promising oxide-based solid-state electrolyte, Li 7 La 3 Zr 2 O 12 (LLZO) has been a research hotspot regarding its phase transition and ion transport mechanisms. Due to the limited unit cell volume of LLZO, few existing studies have investigated the influence of high-entropy multi-element doping on the crystal structure and ion migration pathways of LLZO. The development of machine learning potential functions has provided a powerful tool for high-precision and high-efficiency simulations of complex systems. In this study, molecular dynamics simulations guided by a machine learning-based neuroevolution potential were employed to systematically investigate the effect of high-entropy elemental doping on the ionic conductivity of LLZO. The computational results indicate that high-entropy doping with high-valence elements significantly reduces the Li + diffusion energy barrier. Simulation results indicate that LLZrSnHfCeTaO possesses superior ionic conductivity. Relative to undoped c-LLZO, this multi-component system achieves a substantial decrease in the ionic migration barrier (from 0.399 eV to 0.22 eV) and an increase in the DOS bandwidth (from 3.7 eV to 4.79 eV). The prepared high-entropy garnet electrolyte Li 7 La 3 (Zr 0.2 Sn 0.2 Hf 0.2 Ce 0.2 Ta 0.2 ) 2 O 12 exhibits a reduced activation energy from 0.43 eV to 0.35 eV, an order-of-magnitude increase in room-temperature ionic conductivity, and excellent air stability. This work reveals the microscopic mechanism by which high-entropy doping enhances the ionic conductivity of LLZO, providing important insights for the rational design and controllable synthesis of high-performance solid-state electrolytes.