In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol


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 graphanol (hydroxylated graphane) 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, but requires a very small fraction of the computational cost. We used our DP to calculate proton self-diffusion coefficients as a function of temperature, to estimate the overall barrier to proton diffusion, and to characterize the impact of thermal fluctuations as a function of system size. We propose and test a detailed mechanism for proton conduction on the surface of graphanol. We show that protons can rapidly 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 only takes place as new Grotthuss chains are formed by rotation of one or more hydroxyl groups in the chain. 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 even lower diffusion barriers.

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Supplementary material

Supporting Information: In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol
The supporting information contains: Visualization of graphanol cells, details of DFT, details of CEC, plots of projected phonon density of states, MSD/(4t) plots, visualization of configurations used to compute bond formation energies, details regarding proton hopping energy and hydroxyl group rotation energy, details about proton concentration calculation, comparison of proton conducting materials, details about lattice Monte Carlo simulations, results of CEC trace-back events in graphanol.

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Codes and files for proton conduction graphanol
Documentation and discussion regarding the study of proton conduction in graphanol using deep learning potentials.