We also compare the root mean square error (RMSE) of the fits for both fingerprints. The test set RMSE with ECFP4 fingerprint is 0.131, and the test set RMSE with MACCS fingerprint is 0.161. Thus, we use ECFP4 to train and test our ML models for the rest of this paper. We start with randomly splitting our data set into one of two categories for each gas; one is used for training the ML model, while the other is initially withheld during training. The training data sets were 80% of our total database for each gas, which represents more than 600 polymers for each gas. We then apply the trained model to the remaining 20% of the polymers (test set) and use these data as verification of the model's accuracy. Then, we employ various ML models on the training sets including support vector regression (SVR), k-nearest neighbors (KNN), decision tree, and random forest regression. Next, we compare the predictive power of these popular ML regression models on CO 2 permeability values. First we study SVR, which has the ability to consider non-linearity in the permeability data . SVR results in R 2 value of 0.84 and 0.203 RMSE on the test set. KNN regression, which predicts the target value by local interpolation of the targets associated with the nearest neighbors in the training set, results in R 2 value of 0.822 and 0.242 RMSE on the test set. Then, we employed a decision tree regression model, which uses a tree structure and inference layer to achieve the final decision of the modeling results . Decision tree regression performs better than both the SVR and KNN regression with R 2 value of 0.881 and 0.148 RMSE on the test set.