Although CGMD and DEM simulations have been demonstrated to be useful to model the different battery electrode manufacturing processes providing valuable insights, their computational cost limits the application of these techniques on a real-time scale. Therefore, we need faster modelling techniques that can be inserted in an optimization loop while the battery electrode manufacturing is being executed, so tuning can be done automatically by the production machinery reaching desired performance parameters. In this scenario Machine Learning (ML) techniques have increasingly become a useful tool in the battery manufacturing field. Other applications of ML include: data regression linking input material attributes to output electrode properties, and surrogate models developments keeping a high accuracy in terms of electrode microstructure and properties, but decreasing the computational cost in comparison to the original physics-based models. For example, in a previous work, we have used a time-dependent Convolutional Neural Network (CNN), fed by previously voxelized-DEM simulation data, to develop a surrogate model able to predict the evolution over time when a dried electrode is calendered at a certain compression degree, decreasing the wall time per step from 45 min to 15 sec. In a similar approach Shodiev et al. used data coming from a Lattice Boltzman Method that simulates in 3D the electrolyte filling process of electrodes to train a neural network. Furthermore, Marcato et al. also used a similar approach but for the electrochemical performance evaluation in 3D and as a function of time. Parallelly, hybrid models that integrate two different techniques have proven to be a useful approach in computational science. For example, by coupling ab initio molecular dynamics (AIMD) and classical molecular dynamics (CMD) simulations a balance between computational feasibility and accuracy is achieved. Liu et al. developed a hybrid computational scheme called HAIR, which combines AIMD with CMD reactive force fields to extend the time scale accessible to AIMD by interspersing CMD simulations between the AIMD parts. Hybrid models have also been proposed by coupling ML with a physics-based model. On one side, Physics Constrained Machine Learning (PCML) models can be designed to capture physical dependencies that cannot be captured by purely ML models. One example is to add an output layer with a physical equation to fulfill a physical law, which is not the case of a purely ML model. Other example could be including a physical constraint to avoid predicting parameters with a wrong sign or impose a monotonic behavior in the model. On the other side, Physics-Assisted Machine Learning (PAML) models have been proposed with the same purpose of improving generality and transferability of purely ML models by aiding with physics the purely ML trained models. For mapping capacitance-voltage curves in semiconductors to oxidation and wafer cleaning procedures, Pratik et al. improved data-driven models by using PAMLbased ones. While for determining the dielectric property, Zhou et al. used the Rayleigh approximation method on CNNs with near-field scanning microwave microscopy imaging data. This PAML approach determines the dielectric properties with higher accuracy and with better generalization ability. CNNs have also been physically assisted in their feature input layers with radiative transfer theory knowledge in bathymetric mapping to build a relationship between water depth and spectral information. Also, for predicting the splitting tensile strength in recycled aggregate concrete for building materials, Liu et al. used PAML to improve the efficiency of the construction of ML models by selecting the most important features in a fracture mechanism. It can be highlighted that the use of PAML has the advantage of increasing generalization of purely ML models since they are physically guided, and this has been applied in different applied science fields. To the best of our knowledge, the present article discloses the first-time application of PAML to the LIB electrode manufacturing process.