For MICE, we compare a linear predictive model, Bayesian linear regression (BLR), with a nonlinear predictive model, extremely randomized trees (ERT). By filling in missing gas permeabilities, imputation allows us to train multitask ML models. This improves our models via parameter sharing, which is physically reasonable, as permeabilities between different gases are related for a given membrane chemistry.