However, the relationships between energy barrier, PLD, and diffusion coefficient remain unclear -the relatively weak correlation demonstrated in Figure highlights the important limitations of the Arrhenius law as a general and direct relationship between diffusion coefficient and energy barrier. The aim of the ML model is to build upon this observed correlation by introducing additional geometrical descriptors, and achieve accurate calculation of diffusion coefficients while significantly reducing the time required for predicting the diffusion coefficient of future selective materials. The ML model was trained using 80% of the 4 873 structures that survived all the different filters imposed. We employed a total of 12 descriptors listed in Table to build the model. The selected hyperparameters for the XGBoost model are detailed in Table . an error of about 0.07. It is important to note that the goal here is not to predict the exact values of the diffusion coefficient due to the inherent noise the values generated by MD simulation (about 20% relative error for KAXQIL). Instead, the objective is to determine the order of magnitude of the diffusion coefficient. The proposed model achieves this objective effectively, as illustrated in Figure , where the predicted diffusion coefficient aligns closely with the true values when represented on a log scale.