One of the key findings of this optimization study is that such a simple water model can do well at high pressures without signficantly worsening the behavior at lower pressures. Figure shows MSE in V res over 4,000 water model parameter combinations which were explored during the optimization. As a demonstration of the success of the reweighting approach, the samples generated for just five water models were sufficient to explore the entire parameter space. In Figure , we also see many local minima indicating there exist many water-like models. For the first time, we have pushed down the error in V res to the maximal possible extent in the water model parameter space with this level of theory over the full temperature and pressure range. 3: Distributions of mean square errors in V res estimated for 462 T, P combinations for all the water models visited during the optimization, in addition to results from existing water model parameters (shown in red). Over 4,000 water model parameter combinations were explored during optimization with reweighted samples from five water models. Figures 4, 5 and 6 compare the performance of water models in three different scenarios. In Figure we show the root mean square of errors (RMSE) for six different thermodynamic properties over a large T, P grid, including 462 T (1-100 • C) and P (1-5000 atm) combinations. In Figure we show RMSE for estimated isobaric property curves, i. e. properties are estimated at 1 atm and several temperatures (1-100 • C). In Figure we have shown RMSE for estimated properties at the single T and P point of 25 • C and 1 atm. These data in these figures we can quantitatively and qualitatively compare how accurately water models were able to match experimental property surfaces and points of interest.