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The hydrogen-bond (H-bond) network of high-pressure water is investigated byneural-network-based molecular dynamics (MD) simulations with thefirst-principles accuracy. The static structure factors (SSFs) of water atthree densities, i.e., 1, 1.115 and 1.24 g/cm3 are directly evaluated from512-water MD trajectories, which are in quantitative agreement with theexperiments. We propose a new method to decompose the computed SSF and identifythe changes in SSF with respect to the changes in H-bond structures. We find alarger water density results in a higher probability for one or twonon-H-bonded water molecules to be inserted into the inner shell, explainingthe changes in the tetrahedrality of water under pressure. We predict that thestructure of the accepting end of water molecules is more easily influenced bythe pressure than the donating end. Our work sheds new light on explaining theSSF and H-bond properties in related fields.

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