This NN captures the variations in manufacturing conditions and the changes produced in the target property. For the target dataset (Si-Gra and NMC on one side and GDL 1000 on the other), we freeze this pretrained NN (Figure ) and add an extra layer to train and perform the same task (Figure ). Initially, for the experimental dataset, two NNs are trained using the graphite source dataset (pre-trained NNs). One model predicts electrode density, and the other one predicts electrode mass loading. Then, one additional layer is added to each of the pre-trained NNs, creating two TL approach NNs for each output target (electrode density & electrode mass loading). The NN architecture of all the 4 models are depicted in Table , where the number of trainable parameters is also specified. For both pre-trained and TL approach NNs we use 6 input parameters: AM weight percentage and fraction of solid (solid content) in the formulation; coating speed and comma gap during coating; and roll speed and roll gap during calendering. In a similar way, a feed-forward NN is trained using the GDL200 dataset to predict the geometrical tortuosity by using as input parameters: fiber diameter, fiber concentration, binder concentration, thickness and compression. Then, one additional layer is added to this pre-trained NN to train the TL approach NN in the GDL1000 dataset. These other architectures are presented in Table . Each hidden layer in all six models is activated with the ReLU function. The codes to train these models were written in Python by using TensorFlow, along with other common scientific computing libraries. They were trained in a 13th Gen Intel(R) Core(TM) i7-13700H with 32 GB of RAM.