Machine-Learning Accelerated Computational Spectroscopy Reveals Vibrational Signature of the Oxidation Level of Graphene in Contact with Water

Precise characterization of the graphene–water interface has been hindered by the experimental inconsistencies and limited molecular-level access to interfacial structures. In this work, we present a novel integrated computational approach that combines machine-learning-driven molecular dynamics simulations with first-principles vibrational spectroscopy calculations to reveal how graphene oxidation alters the interfacial water structures. Our simulations demonstrate that pristine graphene leaves the hydrogen-bond network of interfacial water largely unperturbed, whereas graphene oxide (GO) with surface hydroxyls induces a pronounced Δν̃ ≈ 100 cm^–1 redshift of the free OH vibrational band and a dramatic reduction in its amplitude. These spectral shifts in the computed surface-specific sum-frequency generation spectrum serve as sensitive molecular markers of the GO oxidation level, reconciling previously conflicting experimental observations. By providing a quantitative spectroscopic fingerprint of GO oxidation, our findings have broad implications for catalysis and electrochemistry, where the structuring of interfacial water is critical to the performance.