ArticleJACS Au2025
Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: A First-Principles Machine Learning Study.
Article in JACS Au, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Optical Anisotropy as a Probe of Proton Ordering at Ice Surfaces.Journal of chemical theory and computation · 2026Article
- Molecular Mechanisms of Gas-Ice Interfacial Transport: Size- and Charge-Dependent Fractionation during Bubble Close-off.ACS omega · 2025Article
- Structure and Nonlinear Spectra of the Basal Face of Hexagonal Ice: A Molecular Dynamics Study.Molecules (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the molecular-level structure and dynamics of ice surfaces is crucial for deciphering several chemical, physical, and atmospheric processes. Vibrational sum-frequency generation (SFG) spectroscopy is the most prominent tool for probing the molecular-level structure of the air-ice interface as it is a surface-specific technique, but the molecular interpretation of SFG spectra is challenging. This study utilizes a machine-learning potential, along with dipole and polarizability models trained on
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Registered trials
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