  The isotope effects in x-ray absorption spectra of liquid water are studied
by a many-body approach within electron-hole excitation theory. The molecular
structures of both light and heavy water are modeled by path-integral molecular
dynamics based on the advanced deep-learning technique. The neural network is
trained on ab initio data obtained with SCAN density functional theory. The
experimentally observed isotope effect in x-ray absorption spectra is
reproduced semiquantitatively in theory. Compared to the spectrum in normal
water, the blueshifted and less pronounced pre- and main-edge in heavy water
reflect that the heavy water is more structured at short- and
intermediate-range of the hydrogen-bond network. In contrast, the isotope
effect on the spectrum is negligible at post-edge, which is consistent with the
identical long-range ordering in both liquids as observed in the diffraction
experiment.
