To address the problem of simulating diffusion in both the normal and superionic regimes, we use Machine Learning Molecular Dynamics (MLMD), training a Gaussian Approximation Potential (GAP)-style Machine Learning Force Field (MLFF), using on-the-fly sampling of reference configurations . The resulting MLFF offers near-DFT accuracy without empirical parameterisation and has a significantly reduced computational cost compared to direct AIMD simulation. This method allows us to perform simulations of thousands of atoms over nanosecond timescales. Crucially, it also enables us to sample diffusive behaviour in both the superionic and normal regimes.