In the present work, we propose a novel hybrid PAML model combining DL and DEM trained with a physics-based dataset arising from the slurry drying simulation leading to the formation of electrode microstructures (Figure ). The dataset consists of 3D electrode microstructures with realistic shape AM previously produced by us through a pure physics-based electrode manufacturing model. The data generated by this physics-based slurry drying model is used to train a CNN optimizing its architecture to predict the 3D electrode microstructure evolution over time during the drying process. To validate the model, we adopt two kinds of metrics, one based purely on data comparison and the second one based on electrode functional analysis. Such a novel hybrid approach, DL-DEM, can significantly decrease the computational cost of the original DEM drying simulations while retaining their accuracy.