In this article, we implemented and tested a third approach to this problem, based on the generation of a large database of self-diffusion coefficients and the application of statistical learning. We first performed explicit molecular dynamics simulations on a subset of the CoREMOF 2019 database in order to compute diffusion coefficients for a specific guest molecule -we have chosen xenon to illustrate our algorithm. We then introduced a gridbased algorithm to calculate the diffusion energy barrier values for the guest migration inside the nanoporous material. We then trained a machine learning model on our database, using as features this energy barrier alongside key geometrical descriptors (like the pore limiting diameter). We found that the final model is sufficiently accurate for the purpose of a fast estimation of the self-diffusion coefficient, showing an RMSE on the log 10 of the diffusion coefficient equal to 0.25. We then discuss the perspectives opened by this new methodology.