Validation metrics were calculated over the testing dataset are MSE, MAE and R 2 score and are reported in Table for the 1000 µm × 1000 µm domain size and in Table for the 2000 µm × 200 µm domain size. In addition to the current model, which consist of a random forest with an Ada boost regressor, we also calculated the validation metrics of two additional models, a pure random forest and a pure Ada boost one. Validation metrics comparison between these 3 models for the 1000 µm × 1000 µm domain size (Table ) and 2000 µm × 200 µm domain size (Table ) indicates that the model with best metrics, regardless the selected domain size, is the random forest with Ada boost, which is the one we use for the upcoming Building upon the established relationship between GDL manufacturing parameters and its properties, this work investigates the impact of these parameters on key GDL performance. As hypothesized, increasing fiber concentration enhances electrical conductivity but compromises porosity, thereby hindering diffusivity. Therefore, achieving optimal GDL properties necessitates a delicate balance between these parameters. Table presents the optimal manufacturing parameters predicted by our developed optimizer framework for 2 domain sizes on 4 different cases. 𝐶 𝑓 1 ,𝐶 𝑓 2 , 𝐶 𝑓 3 , and 𝐶 𝑓 proposed a fiber diameter of 7 µm within the specified range of 6-12 µm. Notably, 𝐶 𝑓 1 predicted a lower fiber concentration (14.22 %) compared to other cases, aligning with the expectation of maximizing diffusivity through a reduced solid concentration percentage. This finding aligns with the correlation matrices presented in Figure . Additionally, it also predicted a thickness of 290 µm and a lower compression ratio of 0.1. These characteristics facilitate easier particle transport along the x and z-axes, leading to lower tortuosity and ultimately, higher diffusivity. In contrast, Cases 𝐶 𝑓 2 𝑎𝑛𝑑 𝐶 𝑓 3 exhibited higher fiber concentration values (17 %). This translates to increased electrical and thermal conductivities due to the presence of more conductive materials within the GDL. Furthermore, a higher compression ratio, as observed in these cases, enhances contact points between conductive materials, further enhancing conductivity. Finally, case 𝐶 𝑓 4 displayed manufacturing parameters comparable to cases 𝐶 𝑓 2 and 𝐶 𝑓 , but with a lower fiber concentration. This approach represents a compromise, attempting to balance diffusivity and conductivity requirements. This analysis underscores the importance of carefully considering the interplay between GDL manufacturing parameters and their impact on GDL properties for optimal PEMFC performance. Furthermore, 2000 µm x 200 µm x thickness presented a slightly higher binder concentration for the 𝐶 𝑓 2 and a slightly lower thickness, along with 𝐶 𝑓 . This is to increase the contact points between the solid materials (carbon fiber and PTFE) in the GDL, consequently increasing electrical and thermal conductivities. Figure presents radar charts depicting the associated GDL properties for each of the four investigated scenarios. The optimal manufacturing parameters from Table were used as inputs for a stochastically generated model, and the results were compared with those obtained from a deterministic model, shown in Table .