  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, $\Sigma3(1\bar{1}2)[110]$, $\Sigma3(\bar{1}11)[110]$,
and one twist GB, $\Sigma5(001)[001]$. 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.
