On the other hand, the DNN ensemble learns a model with train R 2 s of around 0.87 on descriptors and 0.89 on fingerprints, which generalizes very well to test R 2 s of approximately 0.89 for both inputs. The similarity between train and test R 2 s for the DNN ensembles suggests that the model is very generalizable and learns the underlying functional relationship between chemistry and permeability. In Fig. of Supporting Information, we note that the DNN ensemble generally predicts permeability reasonably well, though there are some outliers at low or high permeability.