ML has been employed to assign partial atomic charges in previous studies, but these studies mostly focused on isolated molecules, small clusters, and unit cells of crystals. In our case, the ML model needs to capture the dependence of the partial atomic charges of water on its local molecular environment, and we chose atom-centered symmetry functions from the KRR model with a fourth-degree polynomial kernel is 0.00445e (0.00359e) with a coefficient of determination (R 2 ) of nearly 0.9. The performance of the ANN model using ACSFs as the descriptors is similar, and the test MAE for the oxygen atom is 0.00659e, larger than that from KRR. Thus, KRR is the preferred ML model, and for the remainder of this study we present the results generated with the KRR model. The details of the ML model training, including the MD simulation, quantum chemical calculations, ACSFs, KRR, and ANN, are given in the SI.