Ab initio-based deep potential simulation of 2D confined water

Due to the wide range of applications in technological and living systems, the structure and dynamics of water under two-dimensional (2D) confinement have been the focus of several experimental and theoretical studies. In particular, the confinement of water in graphite nanochannels has received considerable attention due to its ability to provide a fundamental understanding of the behavior of water at interfaces. However, from a computational point of view, such a study remains challenging due to the very high cost of calculating physical observables that can be compared against experiments. Here, we apply a first-principles-based deep neural network potential (DP) that enables us to simulate water confined between graphite walls with quantum accuracy, and over time and length scales well beyond the reach of conventional first-principles methods. From the DP molecular dynamics simulation, we calculate the infrared spectra. In addition, it allows us to calculate the structure factors, which along with other structural and dynamical properties, provide insight into water under 2D confinement.