We see from the different representations that while both PLD and barrier energy play an important role in the diffusion coefficient, they are also complementary: the information obtained through energy calculations strengthens our comprehension of the correlation, compared to a purely geometric description. Indeed, PLD values cannot distinguish between structures over 6 Å in the "plateau" region of the diffusion/PLD graph. While a geometric analysis would interpret the different values of diffusion coefficient as statistical noise, Based on this microscopic understanding, we then decided to combine standard geometrical descriptors with energy barrier values to train a machine learning model, following the approach we had previously used for high-throughput screening of thermodynamics of Xe/Kr separation . We describe and analyse this ML model in the next section, and show how it can be used to evaluate the diffusion coefficient of xenon in nanoporous materials, offering a significantly faster alternative to MD simulations.