In fact, the computational costs for calculating observables like energy barriers for solid-state diffusion or diffusivity within the whole electrode can get prohibitive, given the vast configurational space, thus making the complete modeling of all the present processes very costly. Increasing the scale of modeling, two main domains need to be addressed: the mesoscale and the macroscale. At the mesoscale level, ML has already proven to be up to the task when predicting the outcomes of electrode formulation and manufacturing process, from the slurry phase to the final calendering. [24] Such predictions rely heavily on high-quality training data, either experimental or output from physics-based models, namely the ARTIS-TIC pipeline , which was used in the present work to predict electrode thicknesses and porosities. A similar reliance on training data was highlighted for mesoscopic modeling of electrode dynamics upon battery cycling .