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

TL;DR

This paper introduces a machine learning-enhanced computational method combining molecular dynamics and vibrational spectroscopy to accurately determine graphene oxidation levels by analyzing interfacial water structure and spectral shifts.

Contribution

It presents a novel integrated approach that links vibrational spectral shifts to graphene oxidation states, resolving previous experimental inconsistencies.

Findings

Pristine graphene minimally perturbs interfacial water structure.

Graphene oxide causes a significant redshift in free-OH vibrational band.

Spectral shifts serve as molecular markers for GO oxidation level.

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 $\Delta f \sim 100 cm^{-1}$ 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 performance.