Atom-centered neural network approaches have been reported for water for a number of years, of which the "Deep Neural Network" (DNN) architecture, which is trained on snapshots of AIMD trajectories using efficient DFT approach for the potential and forces, is a prominent and versatile example. But even a perfect fit provides a level of accuracy that is below the gold standard CCSD(T) method of electronic structure theory used in the many-body approaches just discussed. Very recent reports using transfer-learning 67 have led to CCSD(T)-transfer-learned FFs for water. These are both atom-centered NN fits, and in both cases were trained on DFT-based samples of condensed phase water consisting of 32 or 64 monomers. Several condensed phase quantities, i.e., radial distribution functions and diffusion constants, were reported using NVT simulations and shown to in very good agreement with experiment. So this recent work perhaps marks a convergence of the MB and atom-centered NN approaches. One graphical example of this is Fig. , which shows the tetrahedral order parameter at room temperature calculated from q-AQUA-pol and the recent NN-transfer-learned potential, as well as from q-AQUA in NVT simlulations. The q-AQUA distributions were smoothed with a gaussian of FWHM=0.05. (The unsmoothed q-AQUA-pol distributions are given in Fig. ) Note all distributions are normalized to one.