(197q) Using Deep Learning Potentials and Graph Lattice Models to Engineer Optimal Proton Conducting Membranes for Fuel Cells

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 an atomistic simulation-based workflow to computationally design fuel cell membrane materials using deep learning potentials (DPs) and graph lattice models (GLMs). Our workflow was used to demonstrate that graphanol (hydroxylated graphane) conducts protons anhydrously with very low diffusion barriers. First, DPs were trained for graphanol that have near-density functional theory accuracy but require a very small fraction of the computational cost. We used these DPs to calculate proton self-diffusion coefficients as a function of temperature, to estimate the overall barrier of proton diffusion, and to characterize the impact of thermal fluctuations as a function of system size. The sensitivity of intrinsic energy barriers to the overall diffusion barrier for proton conduction was further assessed using large-scale GLMs. We discovered, using this workflow, the existence of transient hydrogen-bonded structures, which we indicate as Grotthuss chains, that determine the mechanism of proton conduction in graphanol. We showed 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. The results obtained from the application of our workflow also enable us to propose specific design rules for developing next-generation proton conducting materials with diffusion barriers lower than existing systems.