Finally, several studies used machine learning (ML) to optimize the microstructure of GDL with different goals: For example, Hao et al. investigated the relationship between the thickness of GDL and output power. Several studies have employed machine learning followed by multi-objective optimization techniques to investigate the GDL structure. The machine learning models used were convolutional neural network, extreme learning machine, kernel extreme learning machine, and surface methodology and artificial neural network, respectively. These studies have focused on varying objectives and parameters, leading to a diverse landscape of findings. However, the previously mentioned works were restricted to optimizing only one GDL output (e.g. permeability, current density while minimizing temperature, power density, and again, current density, respectively).