In our approaches herein, we use supervised ML by training feed-forward NNs to the pre-existing data collected from the LIBs cell experiments and the data stochastically generated for GDL, detailed in previous respectively subsections. NNs are selected due to their equation complexity that can learn the underlying non-linear relationships between the manufacturing features and targets. Considering that they also present some limitations, their complex structure can make them prone to overfitting if not carefully regularized and training them often requires large datasets and significant computational resources. To learn the weights of the NNs during training, the Backpropagation algorithm is used. As shown in the schema in Figure , we train a feed-forward NN on the source dataset (Graphite chemistry in LIBs cell experimental dataset and GDL200 in stochastically generated dataset) for predicting a final electrode property (density and mass loading in one case and geometrical tortuosity in the other).