Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: a First Principles Machine Learning Study

TL;DR

This study employs machine learning potentials trained on ab initio data to simulate and interpret vibrational sum-frequency generation spectra of ice surfaces, revealing molecular arrangements and aiding surface chemistry understanding.

Contribution

It introduces a machine learning approach to accurately model and interpret SFG spectra of ice surfaces, linking spectral features to molecular configurations.

Findings

Support for proton-ordered Ice Ih surface structure below premelting.

Assignment of SFG peaks to specific molecular configurations.

Assessment of subsurface layer contributions to SFG spectra.

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 ab initio data, to calculate the SFG spectrum of the air--ice interface. At temperatures below ice surface premelting, our simulations support the presence of a proton-ordered arrangement at the Ice Ih surface, similar to that seen in Ice XI. Additionally, our simulations provide insight into the assignment of SFG peaks to specific molecular configurations where possible and assess the contribution of subsurface layers to the overall SFG spectrum. These insights enhance our understanding and interpretation of vibrational studies of environmental chemistry at the ice surface.