Bagusetty et al. used density functional theory (DFT) to theoretically investigate PT in graphanol and graphamine (amine functionalized graphane). Their study revealed that PT in these materials follows a Grotthuss-like mechanism. However, they were unable to compute PT selfdiffusion coefficients, energy barriers, and long-time PT behavior due to the limitations of DFT. Additionally, the study was limited to materials functionalized on only one side (single-sided graphanol or SSG) to reduce computational complexity. It is worth noting that in practical applications, these materials are synthesized with both sides functionalized (double-sided graphanol or DSG). Achar et al. developed accurate deep learning atomistic potentials (DPs) for graphane and SSG via the DeePMD formalism . Their DPs allowed them to fully characterize PT on SSG, which was not feasible via DFT calculations. These DPs are far more efficient than DFT and exhibit linear scaling, which allows for very long DP-molecular dynamics (MD) simulations. The results showed that SSG has a low PT barrier of 99 ± 9 meV, which is about four times lower than Nafion. However, it remains unclear whether DSG has PT behavior that differs from SSG. Functionalization on both sides of the DSG membrane can influence its flexibility. This change in flexibility might lead to differences in overall PT when compared to SSG. Given that it is more practical to experimentally synthesize DSG, it is crucial to investigate the effect of double-sided functionalization on flexibility and PT.