Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li${}_6$PS${}_5$Cl using machine-learning interatomic potentials

Abstract

Li${}_6$PS${}_5$Cl is a promising candidate for the solid electrolyte in all-solid-state Li-ion batteries. 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 (\textit{ab initio} methods) or accuracy limitations (classical potentials) as challenges. Here, we develop a quality-level-based active learning scheme for efficient and systematic development of \textit{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 Li${}_6$PS${}_5$Cl and investigate two tilt GBs, $\Sigma 3(1\bar{1}2)[110]$, $\Sigma 3(\bar{1}11)[110]$, and one twist GB, $\Sigma 5(001)[001]$. All three GBs exhibit low formation energies of less than \SI{20}{meV/\angstrom\textsuperscript{2}}, indicating their high stability in polycrystalline Li${}_6$PS${}_5$Cl. 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. At \SI{300}{\kelvin}, the GB diffusion coefficients fall between the ones of the anion-ordered bulk structure (\SI{0.012e-7}{cm^2/s}, corresponding ionic conductivity about \SI{0.2}{mS/cm}) and the anion-disordered bulk structure (\SI{50}{\percent} Cl/S-anion disorder; \SI{2.203e-7}{cm^2/s}, about \SI{29.8}{mS/cm}) of Li${}_6$PS${}_5$Cl. Experimental data fall between the Arrhenius-extrapolated diffusion coefficients of the investigated atomic structures.

26 pages, 14 figures, 5 tables

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Cited by in corpus (1)

• Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions