To evaluate the accuracy and transferability of each model, we first compare the training error on pentamers and the testing error on octamers sampled from bulk PIMD simulation and plot the results in training set, we achieve an accuracy of 0.044±0.004, 0.029±0.001, 0.029±0.001, 0.030±0.001, 0.030±0.002, 0.045±0.003 kcal• mol -1 /atom, for model 1 to model 6, respectively. Apparently, most models show similar fitting capabilities: most models reach a good accuracy of ~0.030 kcal• mol -1 /atom, better than previously reported range-separated models such as TensorMol (which features a RMSD of 0.054 kcal• mol -1 /atom for water). The only two show significantly worse training results are the pure EANN model without any long-range terms and the one with only isotropic electrostatics. Meanwhile, much better fitting RMSD (0.022 kcal• mol -1 /atom) can be obtained with a larger cutoff of 6 Å for these two models. These results show that while the anisotropicity of the electrostatics (i.e., the high order multipoles) is a local effect, it still plays a nonnegligible role up to 6 Å in the water system. Therefore, the quality of a range-separated hybrid force field relies on a good balance between the cutoff distance of the ML model and the order of truncation in the long-range electrostatics. A careful treatment of the longrange electrostatics can help to increase the locality of the ML model, thus significantly reducing its dimensionality and computational cost. Furthermore, a smaller cutoff for the ML model also means that we can train it with much smaller clusters, the importance of which cannot be overemphasized considering the poor scaling of CCSD(T).