The performance of the model was further assessed on the single dataset for each zeolite-type and on each of the four considered structure deformations. The whole dataset exhibited a mean absolute error (MAE) on forces of 32.2 meV Å -1 while for the single frameworks datasets the MAE ranges between 29.8 and 34.4 meV Å -1 . Concerning the errors for the energies per particle, the MAE for the training set is 0.51 meV while for the single framework datasets is of 0.46 meV or below, with the exception of JNT and WEI with higher MAE values (of 1.38 and 1.51 meV, respectively). As comparison, with another MLIP recently developed with DeePMD-kit 20 on pure siliceous zeolites, the reported MAE on forces and energies were respectively 39 meV Å -1 and 7.8 meV per particle, on a training set of about 280,000 configurations from AIMD simulations on 219 different topologies from the IZA database . Another work adopted SchNet 21 to train a MLIP adopting both quartz polymorphs, well know zeolites topologies and zeolites extracted from the Deem database , reporting MAE values for energies of 155 meV Å -1 and 3.83 meV per particle. By inspecting the metrics for the dataset of each deformed framework, we see that the accuracy may change with the volume of the unit cell, within the same framework type. For most cases, the deformed structures with the largest strain -lowest volume (labeled "2") and with the largest volume (labelled "3") -exhibit the highest and the lowest MAE on the forces, respectively. A similar trend can be observed with the MAE of the energies, although with more fluctuations. Within the two frameworks with the highest MAE, JNT and WEI, the internal differences between the deformed structures are more striking than the other case. and.