Agreement with available experimental data is uniformly excellent, especially for the temperature dependence of the density, which has been a major challenge for first-principles theory and to some extent for the MB-pol potential. The tetrahedral order parameter distributions from q-AQUA-pol and q-AQUA (shown here for the first time) are in very good agreement with one from a recent transfer-learned Neural Network potential 69 at 298 and 300 K, respectively. The ∆-machine learning approach taken here and described earlier 30 appears to be general, and it would be straightforward to use in any polarizable water potential.