We have developed a highly accurate and efficient DP for modeling DSG, which we show is a promising material for use as an anhydrous proton conducting membrane. We demonstrated that our DP accurately predicts energies, forces and phonon properties with near-DFT accuracy. Our training process involved generating many candidate DPs with active learning. Our DP training strategy used initial DFT data to target specific features of the material. Each iteration of active learning improved the DP's robustness by exploring a wider configuration space.