Structure discovery in Atomic Force Microscopy imaging of ice

Abstract

The interaction of water with surfaces is crucially important in a wide range of natural and technological settings. In particular, at low temperatures, unveiling the atomistic structure of adsorbed water clusters would provide valuable data for understanding the ice nucleation process. Using high-resolution Atomic Force Microscopy (AFM) and Scanning Tunnelling Microscopy, several studies have demonstrated the presence of water pentamers, hexamers, heptamers (and of their combinations) on a variety of metallic surfaces, as well the initial stages of 2D ice growth on an insulating surface. However, in all these cases, the observed structures were completely flat, providing a relatively straightforward path to interpretation. Here, we present high-resolution AFM measurements of several new water clusters on Au(111) and Cu(111), whose understanding presents significant challenges, due to both their highly 3D configuration and to their large size. For each of them, we use a combination of machine learning, atomistic modelling with neural network potentials and statistical sampling to propose an underlying atomic structure, finally comparing its AFM simulated images to the experimental ones. These results provide new insights into the early phases of ice formation, which is a ubiquitous phenomenon ranging from biology to astrophysics.

References in corpus (13)

• Van der Waals Density Functional for General Geometries
• Van der Waals density functionals applied to solids
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Efficient implementation of a van der Waals density functional: Application to double-wall carbon nanotubes
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
• A molecular perspective of water at metal interfaces
• The mechanism of high-resolution STM/AFM imaging with functionalized tips
• The origin of high-resolution IETS-STM images of organic molecules with functionalized tips
• Measuring Robustness to Natural Distribution Shifts in Image Classification
• Automated Structure Discovery in Atomic Force Microscopy
• e3nn: Euclidean Neural Networks
• The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
• Machine learning aided atomic structure identification of interfacial ionic hydrates from atomic force microscopy images

Cited by in corpus (2)

• Direct imaging of carbohydrate stereochemistry
• Unveiling the amorphous ice layer during premelting using AFM integrating machine learning