Gaussian approximation to single particle correlations at and below the picosecond scale for Lennard-Jones and nanoparticle fluids
Bibliographic record
Abstract
To describe short time (picosecond) and small scale (nanometre) transport in fluids, a Green's function approach was recently developed. This approach relies on an expansion of the distribution of single particle displacements around a Gaussian function, yielding an infinite series of correction terms. Applying a recent theorem (van Zon and Cohen 2006 J. Stat. Phys. 123 1–37) shows that for sufficiently small times the terms in this series become successively smaller, so that truncating the series near or at the Gaussian level might provide a good approximation. In this paper, we derive a theoretical estimate for the time scale at which truncating the series at or near the Gaussian level could be supposed to be accurate for equilibrium nanoscale systems. In order to numerically estimate this time scale, the coefficients for the first few terms in the series are determined in computer simulations for a Lennard-Jones (LJ) fluid, an isotopic LJ mixture and a suspension of a LJ-based model of nanoparticles in a LJ fluid. The results suggest that for LJ fluids an expansion around a Gaussian is accurate at time scales up to a picosecond, while for nanoparticles in suspension (a nanofluid), the characteristic time scale up to which the Gaussian is accurate becomes of the order of 5–10 ps.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".