We train multitask RF and DNN models to predict gas permeabilities based on chemical descriptors and fingerprints, with 20% of the data reserved for the test set and the remaining 80% used for training, selected randomly. RFs reduce variance in decision trees by making regression predictions based on the average of many decision trees: during the growth of each tree, each new decision rule is made using only a random subset of datapoints and features . RFs thus build generalizable models of non-linear relationships. We train each RF using 200 estimators, with training capped at the square root of the number of features for each decision tree and a max tree depth of 10. Additionally, we train dense multilayer perceptrons. These DNNs have 5 hidden layers with 64, 64, 32, 16, and 8 nodes, respectively; ReLU activation; and dropout of 0.1. The multitask models output 6 permeabilities for the 6 gases of study. The DNNs are trained using minibatch gradient descent with a batch size of 64, the Adam optimizer, and mean-squared-error loss.