  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-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, 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 the
complicated solid-water systems more rapidly and at a lower computational cost
with the aid of ML.
