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.