In these regards, computational 3D modelling has been proven to be an efficient tool to better understand battery phenomena at the mesoscale , where relationships between the electrode microstructure and performance can be elucidated. Concerning the generation of 3D electrode microstructural models, we can distinguish between two main approaches, each with their own advantages and disadvantages: (i) stochastic generation, where particles are randomly placed or piled on a simulation box until a certain target is met (porosity, particle number, etc.) and (ii) physics-based simulations, such as Coarse-Grained Molecular Dynamics (CGMD) and Discrete Element Method (DEM) . A recently developed third option consists of generating realistic 3D electrode microstructures by generative adversarial networks (GANs) . Those models can then be fed to electrochemical models in order to assess their performance as a function of their microstructural properties .