Fast Proton Transport on Graphanol: Mechanistic Insights from Machine Learning and Lattice Models

Development of new materials capable of conducting protons in the absence of water is crucial for improving the performance, reducing the cost, and extending the operating conditions for proton exchange membrane fuel cells. We present detailed atomistic simulations showing that hydroxylated graphane, which we call graphanol, will conduct protons anhydrously with very low diffusion barriers. We developed a deep learning potential (DP) for graphanol that has near-density functional theory accuracy at a small fraction of the computational cost. We used our DP to calculate diffusion coefficients, estimate the diffusion barrier, and compute thermal fluctuations as a function of system size. We identified the mechanism for proton conduction on the surface of graphanol. We show that protons hop along Grotthuss chains containing several hydroxyl groups aligned such that hydrogen bonds allow for conduction of protons forward and backward along the chain without hydroxyl group rotation. Long-range proton transport takes place as new Grotthuss chains form by rotation of hydroxyl groups. Thus, the overall diffusion barrier consists of a convolution of the intrinsic proton hopping barrier and the intrinsic hydroxyl rotation barrier. Our results provide a set of design rules for developing new anhydrous proton conducting membranes with low diffusion barriers.