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  • Open Access

Unveiling the Amorphous Ice Layer during Premelting Using AFM Integrating Machine Learning

Binze Tang1, Chon-Hei Lo1,2, Tiancheng Liang1, Jiani Hong1,*, Mian Qin3, Yizhi Song1, Duanyun Cao4,5, Ying Jiang1,6,7,8,†, and Limei Xu1,6,7,‡

  • *Contact author: timeless@pku.edu.cn
  • †Contact author: yjiang@pku.edu.cn
  • ‡Contact author: limei.xu@pku.edu.cn
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Phys. Rev. X 15, 041048 – Published 11 December, 2025

DOI: https://doi.org/10.1103/9fzf-y9n9

Abstract

Premelting plays a key role across physics, chemistry, materials, and biology sciences but remains poorly understood at the atomic level due to surface characterization limitations. We report the discovery of an amorphous ice layer (AIL) preceding the quasiliquid layer during ice premelting, enabled by a machine learning framework integrating atomic force microscopy (AFM) with molecular dynamics simulations. This approach overcomes AFM’s depth and signal limitations, allowing for three-dimensional surface structure reconstruction from AFM images. It further enables structural exploration of premelting interfaces across a wide temperature range that are experimentally inaccessible. We identify the AIL present between 121 and 180 K displaying a disordered two-dimensional hydrogen-bond network with solidlike dynamics. Our findings refine the ice premelting phase diagram and offer new insights into the surface growth dynamic, dissolution, and interfacial chemical reactivity. Methodologically, this work establishes a novel framework for AFM-based 3D structural discovery, marking a significant leap in our ability to probe complex disordered interfaces with unprecedented precision and paving the way for future disciplinary research, including surface reconstruction, crystallization, ion solvation, and biomolecular recognition.

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Popular Summary

Ice begins to change well before it melts. Even far below freezing, its surface forms a thin, liquidlike layer through premelting—a process relevant in cloud formation, cryopreservation, and planetary science. We provide a machine learning framework that combines high-resolution atomic force microscopy and computer simulations to map the ice surface at the atomic level, identifying an amorphous ice layer. This layer is a disordered, solidlike state that appears between 121 and 180 K prior to the liquidlike layer. Recognizing this previously overlooked phase informs our understanding of ice melting and growth. Our AI-assisted framework offers a promising approach to exploring other problems in materials and surface science, including complex, disordered surfaces, catalysts, functional materials, and biomolecules.

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Phys. Rev. X 15, 041048– Published 11 December, 2025
  • Received 4 July 2025
  • Accepted 16 October 2025
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