To quantify performance, we evaluate the accuracy and generalizability of our ML models, namely RFs and DNN ensembles trained on chemical descriptors and MFFs. For our supervised ML models, the metric of study is the R 2 correlation between the predicted and actual permeabilities on the training and test sets, as summarized in Table . We focus our analysis to models trained on the permeabilities imputed via BLR for consistency. Firstly, we find that the choice of ML model is more important than the choice of chemical features. The average test R 2 across all six gases for the RF is approximately 0.74 when trained on descriptors and very similar when trained on fingerprints. Similarly, the test R 2 values for the DNN ensembles are around 0.90 for both descriptors and fingerprints. We infer that fingerprints offer slightly better performance, which has been observed in the prediction of polymer glass transition temperature . By contrast, we find that the choice of ML model has a significant impact on performance. RF learns a model with train R 2 s of about 0.96 on descriptors and 0.90 on fingerprints, which reduce to test R 2 s of about 0.74 for both inputs. In particular, the RF model seems to struggle to fit the datapoints with very low or very high permeabilities, as demonstrated in Fig. of Supporting Information by the points that have a high actual permeability but lie below the unit line. This would suggest that the RF does not prioritize fitting to the PIMs with high gas permeabilities in the training set, as PIMs make up a relatively small fraction of the training data and tend to have distinct chemistry compared to the rest of the training set.