Digital models illustrating the CL microstructure are essential to understand, via the numerical modelling, of the electrochemical performance of this layer. Stochastic generation is one of the earliest methods used to generate statistically representative microstructures of the CL microstructure. Mukherjee and Wang reported a stochastic pore-solid reconstruction model informed from statistical descriptors such as porosity and two-point correlation functions. This was further improved upon by Kim and Pitsch , who performed a three-phase reconstruction of the CL. Various improvements have been reported since, involving different ionomer contents , off-set distributions of the ionomer , and most recently, the nano-structural reconstruction with explicit representation of the Pt particles . However, the stochastic nature of the process does not allow for the explicit treatment of solvent effects or for the representation of microstructural evolution as a consequence of complex ionomercarbon interactions.