The molecular scale mechanism of deposition ice nucleation on silver iodide.

Heterogeneous ice nucleation is a ubiquitous process in the natural and built environment. Deposition ice nucleation, i.e. heterogeneous ice nucleation that - according to the traditional view - occurs in a subsaturated water vapor environment and in the absence of supercooled water on the solid, ice-forming surface, is among the most important ice formation processes in high-altitude cirrus and mixed-phase clouds. Despite its importance, very little is known about the mechanism of deposition ice nucleation at the microscopic level. This study puts forward an adsorption-based mechanism for deposition ice nucleation through results from a combination of atomistic simulations, experiments and theoretical modelling. One of the most potent laboratory surrogates of ice nucleating particles, silver iodide, is used as a substrate for the simulations. We find that water initially adsorbs in clusters which merge and grow over time to form layers of supercooled water. Ice nucleation on silver iodide requires at minimum the adsorption of 4 molecular layers of water. Guided by the simulations we propose the following fundamental freezing steps: (1) Water molecules adsorb on the surface, forming nanodroplets. (2) The supercooled water nanodroplets merge into a continuous multilayer when they grow to about 3 molecular layers thick. (3) The layer continues to grow until the critical thickness for freezing is reached. (4) The critical ice cluster continues to grow.

Liquid Water and Interfacial, Cubic, and Hexagonal Ice Classification through Eclipsed and Staggered Conformation Template Matching.

We propose a novel method based on template matching for the recognition of liquid water, cubic ice (ice Ic), hexagonal ice (ice Ih), clathrate hydrates, and different interfacial structures in atomistic and coarse-grained simulations of water and ice. The two template matrices represent staggered and eclipsed conformations, which are the building blocks of hexagonal and cubic ice and clathrate crystals. The algorithm is rotationally invariant and highly robust against imperfections in the ice structure, and its sensitivity for recognizing ice-like structures can be tuned for different applications. Unlike most other algorithms, it can discriminate between cubic, hexagonal, clathrate, mixed, and other interfacial ice types and is therefore well suited to study complex systems and heterogeneous ice nucleation.