Machine learning the charges of water - collaboration with the Shi group published!


Collaborative work with the Shi group, led by Bowen Han, is published in J. Phys. Chem. Lett.!  Rigid nonpolarizable water models with fixed point charges have been widely employed in molecular dynamics simulations due to their efficiency and reasonable accuracy for the potential energy surface. However, the dipole moment surface of water is not necessarily well-described by the same fixed charges, leading to failure in reproducing dipole-related properties. Here, we developed a machine-learning model trained against electronic structure data to assign point charges for water, and the resulting dipole moment surface significantly improved the predictions of the dielectric constant and the low-frequency IR spectrum of liquid water. Our analysis reveals that within our atom-centered point-charge description of the dipole moment surface, the intermolecular charge transfer is the major source of the peak intensity at 200 cm–1, whereas the intramolecular polarization controls the enhancement of the dielectric constant. The effects of exact Hartree–Fock exchange in the hybrid density functional on these properties are also discussed.