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Interfacial water is essential in multiple scientific fields, including physics, chemistry, biology, energy, and materials science. Moreover, interfacial ionic hydrates are closely related to various essential issues in both applied fields and natural processes, including electrocatalytic processes[1-4], seawater desalination, biological ion channels, and chemical reactions. Understanding the atomic-level structure of different interfacial hydrogen bond networks, with or without ions, is crucial for elucidating the extraordinary properties exhibited by the water/solid interface in nature. With DFT calculations and AFM image simulations based on experimental data, we investigate the hydrogen bond network structures on various substrates, and analyze their physical and chemical characteristics, such as dynamic properties, proton transfer mechanisms, and the origin of ice pre-melting. Considering that the trial-and-error process of obtaining simulated AFM images that match the experimental image can be time-consuming, we are also exploring the application of machine learning to expedite structure determination with increased efficiency[5]. 

References：

1. Y. Tian*, J. Hong*, D. Cao*, S. You*, Y. Song, et al. "Visualizing Eigen/Zundel cations and their interconversion in monolayer water on metal surfaces", Science 377, 315-319 (2022).

2. D. Cao, Y. Song, J. Peng, et al. "Advances in Atomic Force Microscopy: Imaging of Two- and Three-Dimensional Interfacial Water", Front Chem. 9, 745446 (2021)

3. D. Cao, Y. Song, J. Peng, et al. "Advances in Atomic Force Microscopy: Weakly Perturbative Imaging of the Interfacial Water", Front Chem. 7, 626 (2019)

4. J. Peng, D. Cao, Z. He, et al. "The effect of hydration number on the interfacial transport of sodium ions", Nature 557, 701-705 (2018).

5. B. Tang*, Y. Song*, M. Qin*, et al. "Machine learning aided atomic structure identification of interfacial ionic hydrates from AFM images", National Science Review. nwac282 (2022).