We here study the mechanism for PT in 2-D single-sided graphanol (Figure ) using atomistic deep learning potentials (DPs) that are trained with DFT data via the deep potential molecular dynamics (DeePMD) formalism. We used single-sided graphanol for computational convenience; we envision the real membrane will be double-sided (functionalized on both sides). We use a DP rather than DFT to study PT on graphanol to explore extended time and length scales not accessible with DFT. Our largest graphanol simulations contain over 22000 atoms and cover ns time scales. We estimate the proton hopping barrier for graphanol to be 99 ± 9 meV, which is significantly lower than for Nafion and most other competing proton conducting materials. We found that the PT mechanism in graphanol involves protons moving along Grotthuss chains, that these chains mutate through OH rotations, and that the overall barrier involves a combination of site-to-site proton hopping barriers and OH rotational barriers. We predict that the effective barrier, and hence the diffusion constant, may be tuned in similar materials by manipulating the hopping and rotational barriers.