Molecular Dynamics Simulations of Ice Nucleation by Electric Fields
Bibliographic record
Abstract
Molecular dynamics simulations are used to investigate heterogeneous ice nucleation in model systems where an electric field acts on water molecules within 10-20 Å of a surface. Two different water models (the six-site and TIP4P/Ice models) are considered, and in both cases, it is shown that a surface field can serve as a very effective ice nucleation catalyst in supercooled water. Ice with a ferroelectric cubic structure nucleates near the surface, and dipole disordered cubic ice grows outward from the surface layer. We examine the influences of temperature and two important field parameters, the field strength and distance from the surface over which it acts, on the ice nucleation process. For the six-site model, the highest temperature where we observe field-induced ice nucleation is 280 K, and for TIP4P/Ice 270 K (note that the estimated normal freezing points of the six-site and TIP4P/Ice models are ∼289 and ∼270 K, respectively). The minimum electric field strength required to nucleate ice depends a little on how far the field extends from the surface. If it extends 20 Å, then a field strength of 1.5 × 10(9) V/m is effective for both models. If the field extent is 10 Å, then stronger fields are required (2.5 × 10(9) V/m for TIP4P/Ice and 3.5 × 10(9) V/m for the six-site model). Our results demonstrate that fields of realistic strength, that act only over a narrow surface region, can effectively nucleate ice at temperatures not far below the freezing point. This further supports the possibility that local electric fields can be a significant factor influencing heterogeneous ice nucleation in physical situations. We would expect this to be especially relevant for ice nuclei with very rough surfaces where one would expect local fields of varying strength and direction.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".