Overall, our pre-existing experimental dataset consists of 235 graphite-based, 53 Si-Gra-based and 63 NMC-based electrodes that were fabricated with the conditions specified in the Methods section. Figure shows the distribution of the experimental manufacturing parameters for each chemistry. As global aspects of this distributions we can highlight that Graphite is the most extensive one, in what respects to the range of values considered and amount of data that can be appreciated in the counts y-axis. Then, Si-Gra and NMC have parameters in the same range except for some cases like the AM weight percentage in Si-Gra or solid content also in NMC. This makes the decision to use TL a good choice since it is expected that the extra layer will adapt the model to each of these particularities. The goal of this work is to learn the impact of these six input features on final electrode properties from Graphite-based electrodes and then transfer this learning to Si-Gra-and NMC-based electrodes. The size of these datasets reflects a typical experimental case where the acquisition of them demands a lot of resources. So, the simple TL approach proposed here is aimed at this kind of case recurrent in academic laboratories.