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

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.