The air-water and graphene-water interfaces represent quintessential examples of the liquid-gas and liquid-solid boundaries, respectively. While the sum-frequency generation (SFG) spectra of these interfaces show similarities, a consensus on their signals and interpretations has yet to be reached. Leveraging deep learning, we computed first-principles SFG spectra for both systems, addressing experimental discrepancies. Our findings reveal that similarities in SFG signals do not translate into comparable interfacial microscopic properties. Instead, graphene-water and air-water interfaces exhibit fundamental differences in SFG-active thicknesses, hydrogen-bonding networks, and surface dynamics. These distinctions underscore roughness suppression and electronic interactions present at the solid-liquid interface but absent at the gas-liquid interface.

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• Molecular spectra
• Interfaces
• Machine learning
• Molecular dynamics