The trained MLIP was then assessed on the prediction of the lattice parameters of different zeolitic frameworks, where crystal shapes were constrained in both static DFT and MD simulations. Table reports the comparison between the results of the two methods. We observe that unit cell volumes are predicted with a MAE of 1.21%, considering structures from both training and test set, while errors lower than 0.5% were achieved for 8 out of the 17 frameworks. This displays better accuracy than empirical zeolite force fields used in the field, indicating the advantage of the ab initio data-based force field optimization. Figure shows the DFT and the MLIP computed equilibrium volumes for the different frameworks, including both the training and evaluation sets. We note here that the cubic system RHO exhibited the largest deviation from the DFT results, with a volume deviation of 7.46%. This is a special (and interesting) case because DFT optimization revealed two local minima structures with different volumes, while the MD relaxation resulted in a structure with a volume close to the metastable, lowvolume configuration. Despite this complication, the trained MLIP demonstrated reasonably accurate performance, considering it was trained solely on high-energy structures without specific consideration of the energy minima.