Self-diffusion process in water: Spatial picture of single-particle density fluctuations
Bibliographic record
Abstract
A computer simulation methodology with which to study the single-particle dynamics in complex molecular liquids is presented. Molecular dynamics simulations of liquid water are performed in the temperature range of 238–473 K using the polarizable point charge (PPC) potential. The self part of the van Hove density–density correlation function is calculated. Using the Gaussian approximation of the van Hove function the mean self-diffusion coefficient for the PPC potential is calculated. The singularity temperature for supercooled PPC water, Ts=218 K, estimated from the self-diffusion data appears to agree well with most estimates for real water. In order to elucidate the spatial picture of the single-particle molecular density in this complex liquid and its time evolution, we explicitly resolve the self van Hove function in the local frame of water molecules. The self-diffusion tensor is introduced and numerically evaluated from this spatial (separation and direction dependent) self van Hove function. The fluctuations of the single-particle molecular density in liquid water appear to be spatially anisotropic (nonspherical). At low temperatures these dynamical heterogeneities in liquid water tend to increase.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".