Understanding electrofreezing in water simulations
Bibliographic record
Abstract
Molecular dynamics simulations are used to investigate why external electric fields promote the freezing of liquid water models. It is shown that the melting point of water at a pressure of 1 bar increases significantly when water is polarized by a uniform field. Fields of 1 V/nm and 2 V/nm increase the melting point by 24 K and 44 K, respectively. The increased melting point is mainly due to the favorable interaction of near perfectly polarized cubic ice with the applied field. For a fixed temperature, we demonstrate that the size of the critical ice nucleus decreases with field strength, mostly because the melting point, and hence the true degree of supercooling, is increasing with field. On simulation timescales, ice nucleation is observed at ∼40 K below the field-dependent melting point, independent of the particular value of the field applied. Indeed, we find that even quite highly polarized liquid water retains the characteristic local structures, and the related anomalous properties of water. Our results are obviously relevant to the mechanism of heterogeneous ice nucleation by local surface fields. Local fields will effectively increase the degree of supercooling of locally polarized liquid, decreasing the size of the critical nucleus in the region influenced by the field, hence facilitating ice nucleation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".