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