Simulations of Ice Nucleation by Kaolinite (001) with Rigid and Flexible Surfaces
Bibliographic record
Abstract
Nucleation of ice by airborne particles is a process vital to weather and climate, yet our understanding of the mechanisms underlying this process is limited. Kaolinite is a clay that is a significant component of airborne particles and is an effective ice nucleus. Despite receiving considerable attention, the microscopic mechanism(s) by which kaolinite nucleates ice is not known. We report molecular dynamics simulations of heterogeneous ice nucleation by kaolinite (001) surfaces. Both the Al-surface and the Si-surface nucleate ice. For the Al-surface, reorientation of the surface hydroxyl groups is essential for ice nucleation. This flexibility allows the Al-surface to adopt a structure which is compatible with hexagonal ice, Ih, at the atomic level. On the rigid Si-surface, ice nucleates via an unusual structure that consists of an ordered arrangement of hexagonal and cubic ice layers, joined at their basal planes where the interfacial energy cost is low. This ice structure provides a good match to the atomistic structure of the Si-surface. This example is important and may have far-reaching implications because it demonstrates that potential ice nuclei need not be good atomic-level matches to particular planes of ice Ih or cubic ice, Ic. It suggests that surfaces can act as effective ice nuclei by matching one of the much larger set of planes that can be constructed by regular arrangements of hexagonal and cubic ice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".