And their respctive mean values are 70 µm, 58 µm and 62 µm. For the roll speed we have two possible values of 0.27 m/min and 0.54 m/min. Finally, the solid content varies from 42 % to 66 % for NMC and from 22 % to 42 % for Graphite slurries, since the graphite slurry is more viscous at the same shear rate and formulation due to the choice of the binder, and the solid content for Si slurries are close to the lower values of graphite (from 24 % to 32 %). The final electrode properties considered in this work to predict are density and mass loading, which their distributions are presented in Figure . Electrode density together with the electrode mass loading are important properties that control the final performance of the LIB cells. Higher electrode mass loading and electrode density leads to a higher energy density with a trade-off of a lower power density. The optimization of these properties is always a balancing act and is also highly dependent on the type of electrode. As we can see in Figure , Graphite-and Si-Gra based electrodes are generally less dense than NMC-based electrodes due to the lower density of graphite, which determines the maximum density of the final electrode density. On Figure , the mass loading of NMC-based electrodes is wider and considers larger values than the other two AMs. The higher mass loading is due to two factors. NMC has a higher density than graphite, resulting in a higher mass of AM in the same volume of the electrode. Also, as the solid content of NMC slurry phase is higher, a higher volume fraction of NMC is on the same area of the current collector for the same comma gap compared to Graphite or Si-Gra, resulting in a higher mass loading of NMC electrodes compared to those other electrode chemistries. dataset for both cases of density and mass loading. For both models, it can be seen that the loss decreases with an asymptotic shape until it reaches a plateau for both train and validation sets. This indicates that optimal weights of the NNs are found and there is neither over-nor under-fitting. The following results in plots and tables correspond to evaluations in the test set. Having obtained the good performance of the NNs for graphite, that could capture the relationships between the experimental features and targets, now we extend it to Si-Gra and NMC smaller datasets.