Isotope effects in x-ray absorption spectra of liquid water

TL;DR

This study uses advanced many-body and machine learning techniques to model isotope effects in x-ray absorption spectra of liquid water, successfully reproducing experimental observations and revealing structural differences between light and heavy water.

Contribution

It introduces a combined approach of path-integral molecular dynamics with neural network potentials trained on ab initio data to study isotope effects in water spectra.

Findings

Heavy water shows a blueshifted, less pronounced pre- and main-edge in spectra.

Isotope effects are negligible at the post-edge, indicating similar long-range order.

Theoretical spectra match experimental isotope effects semiquantitatively.

Abstract

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.