Non-monotonic dynamic correlation explored via active microrheology
Bibliographic record
Abstract
Abstract In the study of local and heterogeneous structures in supercooled liquids, microrheology plays a crucial role, offering a closer examination of the mechanical properties at a local level. We concentrate on active microrheology, where an external force drives a probe particle. This technique is employed in the study of a Kob–Andersen mixture, using extensive molecular dynamics simulations. Through active microrheology, we analyze the positional dependence of viscosity, observing how probe particles respond to activation velocity. Utilizing advanced stochastic analysis, we disentangle the deterministic and stochastic components of the local viscosity time series, characterizing its nonlinear and intermittent properties, which indicate heterogeneity. We construct a Langevin equation to model the dynamics of local viscosity and derive its drift and diffusion coefficients from simulation data. Additionally, we investigate the temperature-dependent variations of viscosity dynamics, unveiling their multiplicative and nonlinear nature. We elaborate on how the existence of multiplicative dynamics in viscosity results in the characteristic emergence of heterogeneity within viscosity dynamics. We derive a dynamic correlation length from local viscosity. Moreover, this correlation length shows a non-monotonic dependence on temperature with a maximum at about the Kauzmann temperature.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".