Here each dot is a particular experiment of the experimental dataset. For Graphite experiments, pre-trained NN was used, while for Si-Gra and NMC, the respective TL approach NNs were used. The color of each dot indicates the value of the feature (relative to its distribution) while the x-axis positions indicate the individual impact in the NN prediction (SHAP value). The y-axis of each subfigure is sorted in descending order of importance of each manufacturing parameter. We can highlight that the impact of the manufacturing parameters on the NNs prediction depends on the chemistry. This particularity indicates that the TL approach can modify the relevance of the manufacturing parameters in the pre-trained NN when transferred to a new dataset. While for Graphite and Si-Gra pre-trained and TL approach NNs, respectively, the manufacturing parameters that have a larger influence in the outputs are roll gap, comma gap, AM wt.% and solid content. For the NMC TL approach NN, the three most relevant manufacturing parameters change their order with respect to the previous ones: comma gap, roll gap, and AM wt.%. It can be highlighted that in all cases roll speed and coating speed do not have an impact in the NN's predictions. This can be attributed to their distributions (see Figure ), which are composed of specific values. This should not be misunderstood as meaning that these manufacturing parameters have no impact in the final electrode