  Solid-state sodium batteries require effective electrolytes that conduct at
room temperature. The Na$_3$SbS$_4$ (Pn = P, Sb; Ch = S, Se) family have been
studied for their high Na ion conductivity. The population of Na vacancies,
which mediate ion diffusion in these materials, can be enhanced through
aliovalent doping on the pnictogen site. To probe the microscopic role of
extrinsic doping, and its impact on diffusion and phase stability, we trained a
machine learning force field for Na$_{3-x}$W$_{x}$Sb$_{1-x}$S$_4$ based on an
equivariant graph neural network. Analysis of large-scale molecular dynamics
trajectories shows that an increased Na vacancy population stabilises the
global cubic phase at lower temperatures with enhanced Na ion diffusion, and
that the explicit role of the substitutional W dopants is limited. In the
global cubic phase we observe large and long-lived deviations of atoms from the
averaged symmetry, echoing recent experimental suggestions. Evidence of
correlated Na ion diffusion is also presented that underpins the suggested
superionic nature of these materials.
