The success of applying ML models to design new polymer membranes for gas separation has been comparatively lacking, largely owing to limitations in data availability. al. used experimental gas permeability data to develop a ML model to predict gas separation in polymer membranes . They have successfully identified several polymers for improved CO 2 /CH 4 separation and synthesized two of them to experimentally validate the ML predictions. Yuan et al. used ML algorithms to predict the missing values for the permeability of different gases in the online Polymer Gas Separation Membrane Database of the Membrane Society of Australasia . Yang et al. used the same data set and leveraged ML models to predict gas permeability based on the polymer chemistry . However, a ML model that predicts polymer properties by itself does not lead to the discovery of new polymer membranes with optimal properties. In principle, one can propose many candidate polymers, possibly at random, and use ML to predict their performance. This is obviously not an efficient strategy. A ML "forward model" needs to be coupled with an inverse design/generative algorithm to efficiently explore the polymer material space. Genetic algorithms (GA) are an example of a data-driven inverse design method, which can be effectively coupled with an ML model. Srinivasan et al.