  Feynman path-integral deep potential molecular dynamics (PI-DPMD)
calculations have been employed to study both light (H$_2$O) and heavy water
(D$_2$O) within the isothermal-isobaric ensemble. In particular, the deep
neural network is trained based on ab initio data obtained from the strongly
constrained and appropriately normed (SCAN) exchange-correlation functional.
Because of the lighter mass of hydrogen than deuteron, the properties of light
water is more influenced by nuclear quantum effect than those of heavy water.
Clear isotope effects are observed and analyzed in terms of hydrogen-bond
structure and electronic properties of water that are closely associated with
experimental observables. The molecular structures of both liquid H$_2$O and
D$_2$O agree well with the data extracted from scattering experiments. The
delicate isotope effects on radial distribution functions and angular
distribution functions are well reproduced as well. Our approach demonstrates
that deep neural network combined with SCAN functional based ab initio
molecular dynamics provides an accurate theoretical tool for modeling water and
its isotope effects.
