Isoconfigurational molecular dynamics study of the kinetics of ice crystal growth
Bibliographic record
Abstract
Spontaneous self-assembling, such as formation of molecular crystals, is a fascinating topic for investigation. Ability to initiate and control such transformations promises numerous benefits, but our knowledge of underlying mechanisms of such processes is rather limited. The process of freezing of water is an excellent testing ground for such studies. In this paper we report the results of a systematic molecular dynamics study of ice growth at three different temperatures below the melting point initiated from a number of initial interface structures within the isoconfigurational ensemble. It is shown that a specific structure at a growing ice-water interface is able to affect the growth process over a time scale of 1-2 ns. This structural effect can be characterized in terms of relative growth propensities. On the basis of the differences in the shape between isoconfigurational rate distributions and the rate distribution typical of the specific temperature several different kinds of relative growth propensities have been identified. The initial interfacial configurations employed in this work have been assigned using the proposed classification and possible mechanisms of propensity realization have been suggested for selected cases. Results reported in this paper clearly indicate that local structure effects can have significant impact on tendency for a particular ice surface to grow (or melt). The structural effect on ordering propensities is, most probably, a more universal behaviour and might be expected to be seen in other similar problems such as, for example, protein folding.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".