In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol

Abstract

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 selfdiffusion 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.

Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions

“…It has been shown that graphanol is a promising material for use in proton exchange membrane fuel cells [ 21 , 22 , 32 , 33 ]. As such, graphanol may either be neutral or positively charged due to the addition of protons.…”

“…The system that we considered is c24C graphanol, as shown in Figure 6 a, which contains a single added proton to one side of the material. We generated a series of 21,400 configurations from a deep learning atomistic potential classical MD simulation [ 21 ]. For each of these configurations, we obtained based on .…”

“…We generated our training data by randomly sampling configurations from density functional theory molecular dynamics (DFT-MD) simulations. We performed simulations for five different systems: a bulk metal (Cu), a semiconductor (crystalline Si), a wide band-gap insulator (LiF), a molecular fluid (water), and a 2-D system (graphanol, or hydroxyl-functionalized graphane [ 21 , 22 , 32 , 33 ]). These calculations were performed using the Quickstep [ 34 ] module in the CP2K package [ 35 ].…”

“…Our method can also model charged systems due to the addition of a special constraint that maintain the number of electrons in the system. This is of particular importance because it allows the use of our DeepCDP models in conjunction with ML potentials that we have generated for charged systems, such as hydroxyl-functionalized graphane (graphanol) [ 21 , 22 ]. This allows us to have a framework that can compute dynamics, geometries, energetics, and electron densities with near-DFT accuracy at a very small fraction of the computational cost required for DFT calculations.…”

Trifluoroacetic acid molecules confined into/onto metal–organic frameworks using H2btzip for efficiently improving proton conductivity using a synergistic effect

“…1–6 In particular, solid-state proton conductors (SSPCs), as an important component of PEMFCs with broad applications from portable devices to vehicles, can transform chemical energy into electrical energy and have received significant attention. 7–10 Commercial Nafion membranes can achieve a high conductivity of 10 −1 –10 −2 S cm −1 , exhibit excellent chemical stability under mild conditions (98% RH and 60–80 °C), and have become the most widely used proton conducting materials in the market. 11–14 However, poor working conditions ( e.g.…”