Given the different orders of magnitude of the different properties, the deterministic learning was trained on data previously normalized allowing to fit each property value into [0, 1], avoiding any bias induced by the property values. For the optimization we used a Nelder-Mead (NM) method together with a grid search (GS) approach. The NM method minimizes the objective function, 𝐶 𝑓 ,by following an algorithm based on simplex, checking its evaluation at each vertex of the simplex. In order to improve the optimized result and trying to decrease the chances of finding local minima, we also used a GS approach. GS is an iterative algorithm that will split the input data in various combinations exploring the output in all of them. Since our input data is a 5D dataset given the 5 input parameters, we split the range of each input parameter in 4, forming a grid of 4 5 = 1024 points, on each of these point the NM method is performed, keeping only the minimum value of the objective function. The ML regression model and optimization model were developed on Python using the Scikit-learn package. Summarizing, in this study we employed a machine learning approach to optimize the GDL properties for fuel cell applications. The model, as schematized in Figure , considers five input parameters and predicts 7 GDL properties as outputs, including electrical and thermal conductivities (in-plane and through-plane), diffusivity conductivities (in-plane and through-plane), and GDL-MPL contact resistance. The complexity of the problem arises from its multi-dimensional nature, with several interdependent parameters.