Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

YL Ou and Y Ikeda and L Scholz and S Divinski and F Fritzen and B Grabowski, PHYSICAL REVIEW MATERIALS, 8, 115407 (2024).

DOI: 10.1103/PhysRevMaterials.8.115407

Li6PS5Cl is a promising candidate for the solid electrolyte in all- solid-state Li-ion batteries due to its high ionic conductivity. In applications, this material is in a polycrystalline state with grain boundaries (GBs) that can affect ionic conductivity. While atomistic modeling provides valuable information on the impact of GBs on Li diffusion, such studies face either high computational cost (when using ab initio methods) or accuracy limitations (when using classical potentials) as challenges. Here, we develop a quality-level-based active learning scheme for efficient and systematic development of ab initio -based machine-learning interatomic potentials, specifically moment tensor potentials (MTPs), for large-scale, long-time, and high-accuracy simulations of complex atomic structures and diffusion mechanisms as encountered in solid electrolytes. Based on this scheme, we obtain MTPs for Li6PS5Cl and investigate two tilt GBs, E 3(11 2 )110 and E 3(1 11)110, and one twist GB, E 5(001)001. All three GBs exhibit low formation energies of less than 20 meV/& Aring;2, indicating their high stability in polycrystalline Li6PS5Cl. Using the MTPs, diffusion coefficients of the anion-ordered and anion-disordered bulk, as well as the three GBs, are obtained from molecular dynamics simulations of atomistic models with more than 16 000 atoms for 5 ns. At 300 K, the GB diffusion coefficients fall between the ones of the anion- ordered bulk structure (1.2 x 10-9 cm2/s, corresponding to ionic conductivity about 0.2 mS/cm) and the anion-disordered bulk structure (50% Cl/S-anion disorder; 2.2 x 10-7cm2/s, about 29.8 mS/cm) of Li6PS5Cl. Experimental data fall between the Arrhenius-extrapolated diffusion coefficients of the investigated atomic structures, supporting our quantitative in silico predictions.

Return to Publications page