After training our RF and DNN ensemble ML models, we use the models based on MFFs for high-throughput screening and discovery of high-performance polymers for gas separations. We choose the ML models using MFFs for simplicity due to their slightly better performance and lower memory requirements. We calculate MFFs for millions of hypothetical polymers in Datasets B, C and D, which span a wide and relevant chemical space. These inputs are then passed through the RF and DNN-ensemble models in a feed-forward manner. The predicted permeabilities for the DNN model, broken down by dataset, are plotted for O2/N2, CO2/CH4, CO2/N2, and H2/CO2 separations in Fig. . Similarly, the predictions for the RF model are visualized in Fig. of Supporting Information.