Characterization of the Hydrogen-Bond Network in High-Pressure Water by Deep Potential Molecular Dynamics

Abstract: The hydrogen-bond (H-bond) network of high-pressure water is investigated by neural-network-based molecular dynamics (MD) simulations with the first-principles accuracy. The static structure factors (SSFs) of water at three densities, i.e., 1, 1.115 and 1.24 g/cm3 are directly evaluated from 512-water MD trajectories, which are in quantitative agreement with the experiments. We propose a new method to decompose the computed SSF and identify the changes in SSF with respect to the changes in H-bond structures. We find a larger water density results in a higher probability for one or two non-H-bonded water molecules to be inserted into the inner shell, explaining the changes in the tetrahedrality of water under pressure. We predict that the structure of the accepting end of water molecules is more easily influenced by the pressure than the donating end. Our work sheds new light on explaining the SSF and H-bond properties in related fields.