We have used this data as a baseline for testing other methods, such as the calculation of diffusion activation energy, and the training of a machine learning (ML) model based on structural and energetic descriptors. The final ML model demonstrates promising performance, achieving a root mean squared error (RMSE) of only 0.25 on the base-10 logarithm of the diffusion coefficient. This indicates the ability to accurately assess the order of magnitude of diffusion properties. Such assessment can help identify potential diffusion limitations in promising materials and optimize this property to improvement the performance of materials for adsorption-based separations. Furthermore, the techniques developed in this study, as well as future developments, can also be applied to membrane separation processes.