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

Abstract

Precise characterization of the graphene/water interface has been hindered by 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 interfacial water structure. 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 $\Delta f\sim 100c{m}^{-1}$ 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 performance.

23 pages, 3 figures

References in corpus (5)

• Electric Field Effect in Atomically Thin Carbon Films
• Separable Dual Space Gaussian Pseudo-potentials
• CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Revealing the molecular structures of a-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy

Cited by in corpus (1)

• Water structuring at stacked graphene interfaces unveiled by machine-learning molecular dynamics