Besides being used to fit the three-body potential in MB-pol, ML by itself has become a compelling tool to build high-dimensional PES in the past decade. A fairly common practice is to decompose the total energy into a sum of atomic energies, and develop a ML model to predict the atomic energy according to its local environment. In this way, the resulting ML model is size-extensive and can be conveniently applied to systems with different numbers of atoms. Many-body effects and anisotropicity, which are difficult to describe using simple analytical functions, can be easily incorporated under the framework of ML. Such scheme has been extensively used to study water properties, achieving satisfactory results in many applications. However, such ML method relies on localized descriptors, which has limited capability in describing the long-range interactions and is almost certainly unable to give the correct asymptotic behavior of the potential. The long-range interactions are often incorporated implicitly via fitting the model to bulk data, which may cause two potential issues: 1. Bulk reference data is needed, so this method is largely limited to DFT level of theory; 2. Transferability across different chemical and physical environments can be problematic. Therefore, range-separated methods have been employed to improve the performance of ML, with conventional electrostatic and dispersion terms used in conjunction with the short-range ML model. However, the existing range-separated models still use relatively crude long-range potential: electrostatics are often treated at fixed point-charge level, and the dispersion are often directly taken from the D correction used in the reference DFT+D methods. Most importantly, they are typically still fitted to DFT, which is not accurate enough for many problems in the studies of organic materials and biomolecules. We note that the utilization of high-level CW methods is a game changer in the force field development: we have to train the model in much smaller clusters, thus the extrapolation of the model to larger systems is much harder and much more important.