Unveiling the amorphous ice layer during premelting using AFM integrating machine learning

TL;DR

This study uncovers a previously unknown amorphous ice layer during premelting by integrating machine learning with AFM and simulations, revealing new structural insights at atomic levels.

Contribution

It introduces a novel AFM-based 3D structural reconstruction framework using machine learning, enabling exploration of premelting interfaces across inaccessible temperature ranges.

Findings

Identification of the amorphous ice layer between 121-180K

Disordered hydrogen-bond network with solid-like dynamics

Refinement of the ice premelting phase diagram

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 a novel amorphous ice layer (AIL) preceding the quasi-liquid layer (QLL) 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 121-180K, displaying disordered two-dimensional hydrogen-bond network with solid-like dynamics. Our findings refine the ice premelting phase diagram and offering 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.