Meanwhile, more advanced force fields are being developed using function forms beyond conventional force fields. These force fields are typically developed using the bottom-up strategy, in which ab initio energies and forces are the fitting targets. The new generation of force fields can be categorized into two types: the data-driven models and the physics-driven models. The data-driven models include a variety of machine learning (ML) models such as BPNN, DeepPotential, 11 PhysNet, 12 and EANN 13 etc. These ML models are becoming increasingly popular recently and achieved great success in homogeneous and hard material systems. However, the performances of such pure ML methods are less reliable in bulk organic molecular systems. The current ML frameworks do not take the full advantage of the bonding topology information, and a more accurate treatment to the long-range interactions is also in need. Therefore, for molecular systems, ML methods need to be combined with physics-driven models, 14,15 leading to hybrid strategies. For physics-driven models, new terms such as multipolar interactions, polarization effects, and charge transfer effects are introduced into the model analytically. Due to the complexity of these interactions, the implementation of the corresponding forces and the virial tensors is an extremely tedious task, which impedes the fast validation of the model in MD. The situation becomes even worse for hybrid ML/physics-driven models, which are not well-supported in all the currently available MD programs.