In the future, we will focus on the short-range part of the force field, on the basis of the model derived in this work. Since the long-range potential is available now, we will be able to train the short-range interactions using small cluster quantum data at the CW level of theory. The active learning strategy, and the fragment conformation samplings achieved in this work also pave the road to the establishment of an efficient sampling algorithm for small fragment clusters. Furthermore, the Pytorch-based force field calculator implemented in this work warrants an easy integration of force field models with the-state-of-the-art ML infrastructures. Combining all these techniques, we aim to build a chemically accurate force field for proteins, and eventually for general organic molecules.