Accurate and rapid computation of capture performance from realistic CO2-selective pores, mimicking those formed in experiments, is needed. Å-scale pores in graphene for CO2 separation are prepared by oxidation involving controlled lattice gasification. The resulting pore edges are passivated with O functional groups. These groups evolve from the chemisorption of a single epoxy followed by an organization of epoxies in a cluster (Supplementary Section 1). When the cluster size is sufficiently large, strain-mediated C-C bond cleavage followed by gasification yields a carbon vacancy defect (pore) with ether and semiquinones (C=O) decorated at the pore edge. Pores formed in this way have yielded attractive carbon capture performances, however, pores terminated with C=O have not been explored by computational studies. Extensive computational studies have been conducted to analyze gas transport from graphene pores since attractive gas separation from graphene pores was first predicted in the year 2009. For simplicity, early studies probed pore edges devoid of functional groups and assumed rigid apertures with a fixed pore-limiting diameter. Other studies probed pore edges passivated with atomic H, N, or O, however, gas transport was still investigated, assuming a rigid aperture with a fixed PLD. When lattice flexibility and vibrational dynamics were included, PLD modulation and enlargement were predicted, resulting in an increased gas permeance. Pores decorated with the C=O group have not been studied. One expects the pore edge C=O group to interact strongly with polar molecules such as CO2 and even flip-flop across the plane of the pore. This is expected to result in a unique occupancy-dependent guest environment, which could strongly control transport and the resulting separation performance. However, such guestmolecule-induced dynamics of C=O have not been studied. Small pores in graphene impose an energy barrier for the translocation of gas molecules. When the energy barrier becomes large, the transport event becomes a rare event in the MD simulations, which makes accurate measurement of gas separation performance challenging. TST, which estimates the transport rate based on a classical energy barrier event, is extremely useful in predicting transport rates and carbon capture performance in these cases. However, TST calculations have been mainly used to predict qualitative trends, and gas flux predicted by TST has not been validated, e.g., with predictions from MD simulations.