In this work, we demonstrate an accurate and cost-effective ML implementation that can effectively explore the ever-expanding design space for polymeric gas-separation membrane materials, by learning their synthesis-property relationships. Firstly, our study reveals that fixed chemical descriptors or fingerprints are both excellent representations for predicting gas permeabilities of polymer membranes. Corroborating our recent benchmark study on polymer glass-transition temperature , we conclude that the choice of chemical representation generally plays a limited role in each ML model's performance, as long as sufficient chemical substructures are captured. Additional features, such as microstructure, could be considered in future ML models, given the importance of microstructural characteristics such as FVEs in solution-diffusion transport theory of membranes . Incorporation of such characteristics as input features has improved metal-organic framework adsorption prediction , compared to using solely chemical descriptors. These microstructural features could be efficiently calculated via MD simulations, as being demonstrated in this work. Alternatively, because high-throughput MD simulations can calculate gas permeabilities with reasonable accuracy, these simulations could also be used to augment the training set or be incorporated into active learning frameworks to reduce the uncertainty of ML models . Nevertheless, we find that using fixed chemical features captures sufficient information to predict the gas permeabilities of the polymer membranes studied here.