The several computational methods that have been proposed to calculation diffusion coefficient values in nanoporous materials can be broken down into two main categories: methods based on molecular dynamics (MD) simulations and those relying on the application of transition state theory (TST). The first approach is probably the most "natural" way, leveraging physically meaningful MD trajectories of adsorbates inside the nanoporous material, but it is computationally intensive. The second approach is much faster, relying only on the potential energy surface instead of computing long trajectories, but represents an important approximation. In this work, we use statistical learning to predict MD-based diffusion coefficients using approximate values coming from a TST-based method, augmented with other descriptors of the nanoporous geometry.