We have demonstrated that a random forest regression model performs best when predicting the gas permeability and selectivity of polymer membranes. Because there is a significant difference in the R 2 values and the RMSEs, we use random forest regression models in the entire framework. However, random forest algorithms, like essentially all data-driven methods, are intrinsically interpolative . They are only suited to optimize properties within the bounds of the data the model was trained on. Models can still generalize, and interpolate "between molecules" in some abstract design space ( ), but they will not make accurate predictions outside of this space. Thus, the performance of the new polymer structures identified in this framework depends on the initial data set and the range of selectivity and permeability values covered. One way to address this challenge is by using computer simulations combined with e.g. an active learning loop to curate a polymer library will enable us to expand the number of data points included in the ML framework. It is important to note that regardless of the datadriven approach, ML models will always have a strong dependency on the initial data that they are trained on. The only way to surpass this main limitation is to move towards active learning algorithms where we give the algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process . New molecules generated as part of the GA procedure could be screened by an uncertainty-quantifying algorithm, and when confidence is low, new simulations can be run to acquire new ground truth data, which can then be used to retrain the model. This is only possible if we use computer simulations (or a very highthroughput, autonomous experiment) to curate the data since we will need to make on-the-fly property estimations with an active learning framework.