In principle, this understanding could be formed using molecular simulations. In particular, adsorption behavior in models of polymers with permanent porosity can be obtained using Grand Canonical Monte Carlo (GCMC) approaches. Self-diffusion coefficients are routinely obtained using molecular dynamics. Nonequilibrium simulations of transport across a model membrane driven by the gradient of chemical potential can be used to directly obtain permeability of species either as single components or mixtures. Therefore, theoretically, it is possible to calculate independently all the terms in Eq. 1 and explore transport properties of the membrane as a function of various parameters. However, this is not the common practice due to the enormous computational cost associated with the direct simulation of permeation across a model membrane in fully atomistic molecular simulations. As a result, a typical molecular simulation study obtains sorption properties (e.g. solubility coefficient) and self-diffusion coefficient, and uses them to derive all other properties of the membrane (permeability, perm-selectivity). In experiments, typically k and S are obtained from independent measurements, and D is derived using the solution-diffusion relation. Another common practice is to measure k and D from the time-lag method and calculate S using the solution-diffusion model. Not surprisingly, this leads to different predictions from the molecular simulations and experiments. In particular, molecular simulations consistently predict lower self-diffusion coefficient values for CO 2 compared to N 2 . Transport diffusion coefficients from the experiments, on the other hand, derived from S and k, show similar values for CO 2 and N 2 , and sometimes even higher transport diffusion rates for CO 2 . This results in molecular simulations typically predicting