Training this kind of model for both of these electrodes with the lack of data leads to overfitting in the respective training set, as shown in Figures and of the Supporting Information and the discussion there. Thus, the aim of this section is to adapt these NNs with the proposed TL approach to the new chemistries. To overcome this problem, we freeze the weight in the architecture connections of the NN for graphite and add an extra hidden layer to each one of the other chemistries with the same number of nodes in the corresponding architecture. So, being only the weights of this layer trained the problem adapts better to the amount of data of Si-Gra and NMC. The need for these additional layers can be justified with the results shown in Table . The pre-trained NN for predicting electrode density gives an error of 4.53 % when evaluated with graphite-based test data. The error of this model increases up to 78.81 % and 56.13 % when tested on Si-Gra and NMC test data. This is because the pre-trained model is extrapolating outside the feature range for which it was fitted, as seen in Figures and. In the next row of Table it can be appreciated a diminution in both Si-Gra and NMC errors, where these errors decrease to 14.53 % and 8.09 %, demonstrating the improvement of the TL approach NN when an extra layer is added. For the NNs to predict electrode mass loading we have a similar behavior being 13.9 % the error when evaluated in graphite test data and increasing to 44.98 % and 70.82 % for Si-Gra and NMC data, respectively. These errors decrease to 3.71 % and 10.61 % after the simple TL approach is applied. Figure shows this in a more graphical way, where individual predictions are plotted against its actual value. In general aspects, in the left column of plots we have the pre-trained NNs, so with the results of the MAPE presented in Table , a good performance is not expected Si-Gra and NMC test data. In the right column we have the transferred NNs, so an improvement is expected with respect to the left column. not modified in the density prediction but they are improved for the mass loading predictions, where even the tendency slope is improved for both chemistries. Thus, the simple TL approach allowed us to generate new models with great accuracy despite the limitation in the size of the data. To add explanation to the predictions of the NNs, we use XAI, which is a technique used in AI to add interpretability and transparency to the usual black-box ML models. In Figure we show the Shapley values for the pre-trained and the TL approach NNs for a randomly selected test experiment for each chemistry. These Shapley values compute the importance of each feature close to a fixed data point when all the other feature values are constant. This allows us to compare the impact of each manufacturing parameter on the final electrode property for the different chemistries when evaluating a given experiment.