The distribution of these Si-Gra and NMC datasets had both similarities and differences with the Graphite dataset, but they were not large enough to train NNs from scratch. This justified the use of TL by adding an extra layer to each one of the Graphite pre-trained NNs and training this extra layer to adapt these models to each one of these new chemistries. In both cases, the performance of the evaluation metrics improved considerably when evaluated with the TL approach NNs predictions. The interpretation of the influence of each parameter in the NNs predictions was also discussed for some test experiments for each chemistry using XAI. The global analysis of Shapley values allowed us to determine the influence of each feature on the final predicted properties and sorted them by relevance, showing that this depended on the type of chemistry.