In our previous studies, we introduced an AI-powered workflow for the automated discovery of polymer membranes for carbon capture. Briefly, this workflow begins with dataset preparation, where polymers are characterized by structural descriptors (using SMILES ), geometrical parameters, and topological variables, generated via a polymer property prediction engine. The resulting machine-readable dataset is organized into feature vectors, which are then used in regression models for training and optimization. A feature vector-based generative model subsequently designs polymer membrane materials with targeted properties, specifically, high CO 2 permeability. In the final validation step, the CO 2 permeability of the generated membranes is calculated through non-equilibrium molecular dynamics simulations to confirm the workflow's predictive accuracy. To fully assess AI-assisted workflows' effectiveness in designing polymers with promising carbon capture properties, it is essential to probe their separation performance through direct simulation.