Such dataset consists of 3D electrode microstructures with realistic active material (AM) particle shapes previously produced by us by utilizing a computational electrode manufacturing simulation workflow combining Coarse Grained Particle Dynamics (CGPD) (simulating the slurry and its drying) and DEM (simulating the calendering of the dried coating). The data generated by this physics-based electrode manufacturing model is used to train a CNN and further optimize its architecture to predict the 3D electrode microstructure evolution over time during the calendering process. For validation, we employ purely data metrics as well as electrode functional metrics to compare predicted vs. targeted microstructures. Such an approach of combining physics-based calendering simulation and DL (CNN), which is the focus of our study, and for the first time in the field of battery manufacturing, will give researchers an innovative and powerful tool to analyze complex electrode image data which can be generated synthetically, through simulations, or experimentally, through in operando tomography techniques. We demonstrate our DL model here as a proof-of-concept of the applicability of CNN for time-dependent physics problems. The presented DL model demonstrates a predictive accuracy evaluated by different metrics discussed in the following sections. Additionally, it allows to study electrode functional parameters at different CD with much less computational cost (DL wall time is 15 seconds per step in comparison to the ~47 minutes per step of the DEM model), keeping its accuracy and paving the way towards highthroughput screening of the influence of calendering conditions on electrode microstructure and its optimization. In the following sections, we discuss the methodology in detail followed by the results obtained and the conclusions of our study.