The first step in training an ML model is to compile a training data set, and in our case we need a reliable approach to assign partial atomic charges of water. In our previous work, we systematically assessed a variety of quantum-chemical charge models by examining their performance in predicting the dipole moments of isolated water, water clusters, and liquid water as well as charge transfer in the water dimer and liquid water. The charge model that had the most robust performance from our benchmark study was the Iterative Hirshfeld model, with the Natural Population Analysis (NPA) model as a close second. In order to capture the local environmental effects on the partial atomic charges of a water molecule (the central water molecule), we found it necessary to include its first two solvation shells of surrounding water molecules explicitly in the quantum-mechanical (QM) region, while the other water molecules were represented by their molecular-mechanical (MM) fixed charges (i.e., the charges in TIP4P/2005). Here, we included the central water molecule and its nearest 22 water molecules in the QM region, which corresponds to the average number of water molecules within the second minimum of the oxygen-oxygen radial distribution function for liquid water. For the electronic structure methods, we employed density functional theory (DFT) and considered the B3LYP functional along with the aug-cc-pVDZ basis set as well as some modified variants of B3LYP with varying percentages of exact exchange as discussed later. Despite its robustness, our QM/MM charge-assigning protocol is computa-tionally expensive given that the QM region includes 23 water molecules in total. Since this calculation needs to be repeated for hundreds to thousands of water molecules over a long MD trajectory for the computation of some dielectric properties, it is highly desirable to develop an ML model to accelerate the assignment of the partial atomic charges.