In contrast to the aforementioned studies, many work try to construct the water potential from ab initio calculations directly without any experimental inputs. The major challenge here is how to extrapolate the model from small clusters to larger ones, or even bulk. Since high-level ab initio (e.g., coupled cluster) data is only available for small systems, such extrapolation is not only aesthetically elegant, but also practically necessary. Recently, two different strategies have shown great success in water: Head-Gordon group treats all the relevant physics (including charge transfer) explicitly based on EDA method, reaching good accuracies in both cluster and bulk calculations. While this is a very promising strategy, whether the charge transfer functional form can be transferred to different types of molecules is still an open question. Moreover, no NQE were considered in their MD simulations, so the apparent good agreements showed in their study might also be an outcome of error cancellation. Another successful model, MB-pol, was proposed by Paesani group based on a strategy of range separation. After carefully separating the long-range parts, the short-range intermolecular interaction is decomposed into two-and three-body terms, which are then fitted using either permutationally invariant polynomials (PIP) or machine learning (ML) techniques. The MB-pol model has been thoroughly tested in both water cluster and bulk simulations and is considered to be one of the most reliable water models available so far. Meanwhile, we note that MB-pol still suffers from a couple of drawbacks: 1. MB-pol (including both its PIP and ML variations) is based on molecular many-body expansion (MBE) method. While such expansion is suitable for small molecules, it becomes very cumbersome for large flexible molecules or polymers, in which cases molecular MBE does not seem to be natural; and 2. For multicomponent systems, the number of trimer types can be large, and MBE may not converge as fast as in water (one may need tetramers or even more terms to converge MBE). These issues are rooted in MBE, thus are potentially also problems for other MBE-based models. Nevertheless, the success of MB-pol demonstrates that a careful range separation scheme is indeed the key to the extendibility of the resulting model from small clusters to bulk simulations.