Machine learning analysis of the parameter-property relationship. The main aim of using machine learning was to investigate and quantify the effect of the water model force field parameters on the resulting dielectric constant, diffusion coefficient, and surface tension, rather than prediction for models not studied here. Therefore, we did not partition the dataset into a training set and test set, and instead used the entire dataset as the training set. Because there were too few Class II, 5-point, or polarizable models in this study, only Class I 3-point and 4-point models were used for the machine learning analysis. This provided 24 water models for machine learning analysis, which was sufficient for surface tension, dielectric constant, and self-diffusion coefficient, but not for density, coordination number, or free energy of solvation (see below), as the range in values of these properties was too small. Figure presents the contribution as well as the effect of descriptors on each property, where the parameters with positive and negative sign led to an increase or decrease in the value of the property, respectively. By comparing these three graphs, it can be seen that the partial charge on the oxygen (O charge) has a significant effect on each property, with more negative values decreasing the surface tension and dielectric constant, and more positive values increasing the diffusion coefficient. Interestingly, the distance between the partial charge and the center of mass of the oxygen (M-O distance) had a large effect on all properties, with a shorter M-O distance associated with a lower surface tension and dielectric constant, and a longer M-O distance associated with a greater self-diffusion coefficient. Critically, no single parameter simultaneously increased or decreased all properties, illustrating the difficulties in parameterizing a water model that reproduces all experimental properties well.