FFs can be categorized into two main families. The most commonly used FFs are known as classical, or non-polarizable, FFs, that include AMBER, 3,4 CHARMM and GAFF. These non-polarizable FFs employ a combination of fixed-charge Coulomb potential and Lennard-Jones interactions to model intermolecular interactions. They are highly efficient numerically, enabling simulations of very large systems over long time scales. 9,10 However, their simple functional form lacks polarization and many-body effects, which are crucial for accurately describing complex phenomena such as pi-stacking or allosteric effects. On the other hand, there are polarizable force fields (PFFs) such as AMOEBA, 13,14 AMOEBA+, CHARMM Drude, 17 and SIBFA. These force fields have been specifically developed to explicitly incorporate polarization and many-body effects. This enhanced flexibility and accuracy comes at a higher computational cost compared to non-polarizable FFs. Nevertheless, PFFs provide a more comprehensive representation of intermolecular interactions and are particularly suitable for studying complex systems. In recent years, significant attention and resources have been devoted to the development of Machine Learning Potentials (MLPs), aiming to bridge the accuracy and generality gap between FFs and ab-initio methods. MLPs employ flexible functional forms from the field of Machine Learning (ML) to accurately fit ab-initio energies or forces. With respect to ab-initio methods, they offer a favorable balance between computational efficiency and accuracy, circumventing the need for empirical functional forms used in FFs. MLPs possess the ability to capture complex interactions, that are challenging to model when using traditional non-polarizable FFs.