Solid–water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establish clear correlations between specific spectral signatures and distinct interfacial water structures, theoretical calculations using molecular dynamics (MD) simulations are required. These MD simulations typically need relatively long trajectories (a few nanoseconds) to achieve reliable SFG response function calculations via the dipole moment–polarizability time correlation function. However, the requirement for long trajectories limits the use of computationally expensive techniques, such as ab initio MD (AIMD) simulations, particularly for complex solid–water interfaces. In this work, we present a pathway for calculating vibrational spectra (IR, Raman, and SFG) of solid–water interfaces using machine learning (ML)-accelerated methods. We employ both the dipole moment–polarizability correlation function and the surface-specific velocity–velocity correlation function approaches to calculate SFG spectra. Our results demonstrate the successful acceleration of AIMD simulations and the calculation of SFG spectra using ML methods. This advancement provides an opportunity to calculate SFG spectra for complicated solid–water systems more rapidly and at a lower computational cost with the aid of ML.
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