Glassy dynamics and a growing structural length scale in supercooled nanoparticles
Bibliographic record
Abstract
We use molecular dynamics simulation to study the relationship between structure and dynamics in supercooled binary Lennard-Jones nanoparticles over a range of particle sizes. The glass transition temperature of the nanoparticles is found to be significantly lowered relative to the bulk, decreasing as N-1/3 with decreasing particle size. This allows the nanoparticles to sample low energy states on the potential energy landscape, and we are able to study their relaxation times, measured in terms of the intermediate scattering function, and their structure, measured in terms of locally favored structures, to low temperatures. Our work shows that the growing relaxation times in the supercooled nanoparticles are coupled with the growth of physical clusters formed from favored local structures in a way that is well-described by the random first-order transition entropic droplet model, but with exponents that are dependent on the nanoparticle size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".