Out of the datasets obtained for the 17 frameworks, 11 were used for training the interatomic potential, while the remaining 6 datasets served as a test set to evaluate the performance of the trained potential and its generalizability across unseen zeolitic topologies -an important quality for the exploration of novel materials. To optimize the potential training procedure, a subset of the dataset comprising data from SOD, BEC, and JST was selected. This subset comprised a total of 12,300 configurations, which were subsequently divided into 11,100 configurations for training and 1,200 configurations for validation in the cross-validation scheme. In all the trainings, the loss function was minimized adopting the same scaled weights on forces and energies.