Machine learning models. We used QSPR (quantitative structure property relationship) 112 algorithms to explore the quantitative relationship between the force field parameters of the water models in this study and the dielectric constant, diffusion coefficient and surface tension. In a conventional QSPR machine learning technique, descriptors are mathematical values which can define the physical and chemical properties of compounds in dataset. In this study, we considered six force field parameters of the water models as descriptors: O sigma, O epsilon, O charge, O-H bond length, H-O-H angle, M-O distance. Also, to consider the effect of 3-point, 4-point, and flexible models on the resultant properties, we applied a 1-hot binary matrix, where the descriptor was given a value of 1 if present and 0 if absent. A schematic of these descriptors is provided in Figure , Supporting Information. To generate the quantitative relationship between dielectric constant, diffusion coefficient and surface tension with water models, we employed the linear algorithm MLREM (Multiple Linear Regression with Expectation Maximization) 113 implemented in the CSIRO-BioModeller package. To evaluate the quality of models the correlation coefficient between experimental and calculated values (by machine learning) for the data set (R 2 ) were computed. The closer the R 2 value was to 1.0, the better the model. In addition to R 2 values, the standard error of estimation (SEE) was calculated, where SEE represents the root mean square error between the calculated and measured values of data points, adjusted for degrees of freedom. Thus, the SEE is a more robust description of model quality than the R 2 value. 119