We note that of course there are many, many existing water models with varying levels of complexity, all parameterized to capture different physical properties, and each with different optimization approaches and each well-suited for different purposes. A review by Guillot gives a good summary of many water models existing at that time. The web resource maintained by Martin Chaplin is also a useful guide to the efforts made historically to model water atomistically. Ouyang et al. have presented a concise, more recent review of water models, and Cisneros et al. recently explored a range of approaches to incorporate more physical complexity into water models. We recognize, in particular, that on the upper end of complexity, there are a number of highly accurate models for pure water. For example, polarizable water models add terms in the Hamiltonian to capture the rearrangement of electronic degrees of freedom, However, accounting for the polarization can significantly increase the computational costs; additionally the polarizable water models do not necessarily combine well with force fields of other molecules which neglect polarization effects, greatly limiting the utility of such models unless the more complex chemistries include polarizability as well. Because of these issues, researchers generally do not include polarization in routine calculations of large molecular complexes. Other approaches to add additional physics, such as multipole expansions, also offer increased accuracy. Yet more complicated and accurate classical water models have been developed, including many-body and machine learned models that have high agreement to quantum mechanical calculations and phase behavior, though the issues with combining these potentials with other molecules at simpler levels of modeling are exacerbated.