Our approach demonstrates successful implementation of an ML-driven GA to design polymer membranes for CO 2 separation, but more importantly, this framework can be used to design polymer structure for any application (e.g. ion separation membranes and polymer electrolytes for batteries), where there is a constrained optimization problem. The main limitation of the current framework arises from its dependence on the initial experimental data. Curating the data with computer simulations is a possible way to overcome this limitation. With better control over the initial data set we will be in a position to explore more sophisticated descriptors and switch to an active learning framework where we make on-the-fly property estimations.