Margaret L Berrens, Marcos F Calegari Andrade, John T Fourkas, Tuan Anh Pham, Davide Donadio
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引用次数: 0
摘要
了解冰表面的分子级结构和动力学对于破译若干化学、物理和大气过程至关重要。振动总频发生(SFG)光谱是探测气-冰界面分子级结构的最重要工具,因为它是一种表面特异性技术,但 SFG 光谱的分子解释具有挑战性。本研究利用机器学习势能以及在 ab initio 数据基础上训练的偶极子和极化率模型来计算气冰界面的 SFG 光谱。在低于冰表面预熔化的温度下,我们的模拟支持冰I h表面存在质子有序排列,与冰XI中的情况类似。此外,我们的模拟还为在可能的情况下将 SFG 峰分配给特定分子构型提供了见解,并评估了次表层对整个 SFG 光谱的贡献。这些见解增强了我们对冰表面环境化学振动研究的理解和解释。
Molecular Fingerprints of Ice Surfaces in Sum Frequency Generation Spectra: A First-Principles Machine Learning Study.
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 I h 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.