In this work, we follow a similar procedure to Kim et al. to design new polymer membranes with the desired selectivity and permeability for CO 2 separation from N 2 and O 2 . First, we start by assembling a library of gas permeabilities corresponding to the experimental studies of various polymers. Next, we train multiple ML models based on various fingerprints to determine which ML model performs the best in predicting gas permeability. Then, we use our ML models to drive a GA for 100 generations and create more than 16000 new polymer structures. We also use different fitness functions to design the best possible polymers given our initial data set.