we ran the GA with an ML model trained with two different fingerprints, ECFP and MACCS keys, and found that the fingerprint used in the ML does not influence the top performing polymers identified from the GA framework. Next, we tried running the GA for an additional 100 generations to see if running the GA for longer will result in better performing polymers, but found that the additional iterations did not result in any improved polymer structures. Finally, we tried multiple fitness functions to optimize the evolutionary trajectory. To optimize both CO 2 /N 2 and CO 2 /O 2 selectivity as well as CO 2 permeability, one needs a fitness function that includes all three metrics. However, the functional form of the fitness function is not clear a priori. We tried the fitness function log (P CO 2 ) × α CO 2 /N 2 × α CO 2 /O 2 and showed that GAs using this fitness function failed to identify new polymer structures that are above both upper bounds. We found that the fitness function based on the actual P CO 2 permeability values