Then, a random forest + Adaboost regression model was implemented to establish a ML regression model between the output/input parameters. The main advantage of the ML model is the tremendous decrease on the computational cost but keeping a good accuracy in comparison with the stochastically reconstructed model. Regarding the computational cost, the ML model requires ~3 seconds wall-time to predict the 7 output properties, when the physics-based calculations require ~3-4 hours wall-time. In terms of accuracy, the ML model is able to predict the 7 output properties with a R 2 score ~95 % in 6 of the 7 properties, and a R 2 score ~90 % for the contact resistance. Given the low computational cost of the ML model (~3 seconds wall-time per iteration), it was then integrated into an optimization framework to calculate the optimal manufacturing parameters to achieve the desired GDL properties, given certain constraints applied on the framework. The selected optimization framework consists of a Nelder-Mead (NM) method together with a grid search approach to identify the optimal set of input parameters that yielded the desired GDL functional properties.