Understanding why constant energy or constant temperature may affect nucleation behavior in MD simulations: A study of gas hydrate nucleation
Bibliographic record
Abstract
Molecular dynamics simulations have been widely used in exploring the nucleation behavior of many systems, including gas hydrates. Gas hydrates are ice-like solids in which gas molecules are trapped in water cages. During hydrate formation, a considerable amount of heat is released, and previous work has reported that the choice of temperature control scheme may affect the behavior of hydrate formation. The origins of this effect have remained an open question. To address this question, extensive NVE simulations and thermostatted (NPT and NVT) simulations with different temperature coupling strengths have been performed and compared for systems where a water nanodroplet is immersed in a H2S liquid. Detailed analysis of the hydrate structures and their mechanisms of formation has been carried out. Slower nucleation rates in NVE simulations in comparison to NPT simulations have been observed in agreement with previous studies. Probability distributions for various temperature measures along with their spatial distributions have been examined. Interestingly, a comparison of these temperature distributions reveals a small yet noticeable difference in the widths of the distributions for water. The somewhat reduced fluctuations in the temperature for the water species in the NVE simulations appear to be responsible for reducing the hydrate nucleation rate. We further conjecture that the NVE-impeded nucleation rate may be the result of the finite size of the surroundings (here the liquid H2S portion of the system). Additionally, a local spatial temperature gradient arising from the heat released during hydrate formation could not be detected.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".