There is great interest is developing solid state lithium electrolytes for use in an all solid-state battery to replace the flammable organic electrolyte. Previous efforts trying to understand the structure-function relationships resulting in high ionic conductivity materials have relied on ab-initio molecular dynamics (AIMD). Such simulations, however, are computationally demanding and cannot be applied to large systems containing more than a few hundred atoms in a reasonable time frame. Herein, we propose using machine learning artificial neural networks (ANN) to supply the forces and energies used during the MD simulations, and to eliminate the need of costly ab-initio force and energy evaluation methods, such as density functional theory (DFT). After carefully training a robust artificial neural network for four and five element systems, we obtain nearly identical lithium ion diffusivities for Li10GeP2S12 (LGPS) when benchmarking the ANN-MD results with DFT-MD. We find that ANN-MD simulations allow the study of systems that require high number of atoms, such as finer resolution of concentrations, grain boundaries, or vacancy/dopant concentrations. To demonstrate the power of the outlined ANN-MD approach we apply it to a chlorine doped LGPS system to calculate the effect of concentrations of chlorine on the lithium diffusivity at a resolution that would be unrealistic to model with DFT-MD. These larger systems may also allow for the study of diffusivities at more moderate realistic temperatures in the future.