  Solid-state ion conductors (SSICs) have emerged as a promising material class
for electrochemical storage devices and novel compounds of this kind are
continuously being discovered. High-throughout approaches that enable a rapid
screening among the plethora of candidate SSIC compounds have been essential in
this quest. While first-principles methods are routinely exploited in this
context to provide atomic-level details on ion migration mechanisms, dynamic
calculations of this type are computationally expensive and limit us in the
time- and length-scales accessible during the simulations. Here, we explore the
potential of recently developed machine-learning force fields for predicting
different ion migration mechanisms in SSICs. Specifically, we systematically
investigate three classes of SSICs that all exhibit complex ion dynamics
including vibrational anharmonicities: AgI, a strongly disordered Ag$^+$
conductor; Na$_3$SbS$_4$, a Na$^+$ vacancy conductor; and
Li$_{10}$GeP$_2$S$_{12}$, which features concerted Li$^+$ migration. Through
systematic comparison with \textit{ab initio} molecular dynamics data, we
demonstrate that machine-learning molecular dynamics provides very accurate
predictions of the structural and vibrational properties including the complex
anharmonic dynamics in these SSICs. The \textit{ab initio} accuracy of
machine-learning molecular dynamics simulations at relatively low computational
cost open a promising path toward the rapid design of novel SSICs.
