The innovative DL model presented in this work can predict an electrode microstructure evolution over time at a given compression degree during calendering and provide the associated final relaxed electrode microstructure. Here we used the formulation of 96 % AM The number of timeframes extracted from the simulations and employed for training is a hyperparameter to be further investigated to evaluate its impact on the accuracy of the predictions. Grid resolution can also be considered as another hyperparameter that will affect the model performance. In the current work we use a 76 × 76 × 125 voxel 3 resolution, resulting in a voxel size of 0.4 micrometer, which is almost four times smaller than the smallest particle in the DEM simulations. However, further exploration might be insightful as larger voxel sizes could provide faster results, but the accuracy of the model can be compromised. Finally, even though the DL model presented in the current work was trained for a specific formulation, transfer learning can be used to train a new DL model with predictive capabilities for other formulations, or even different electrode materials such as graphite, LFP, or blends. In that sense, more training datasets need to be generated and validated prior to the development of the DL model, and this is ongoing work in our group. Furthermore, the nature of the data set, which is a voxelized geometry, makes our approach general. Particularly, it would be interesting to adapt it to time series datasets obtained from 4D-computer tomography. For this reason, we are working on a model demonstration of the applicability of our method to tomography images with time series.