Inherent structures, fragility, and jamming: Insights from quasi-one-dimensional hard disks
Bibliographic record
Abstract
We study a quasi-one-dimensional system of hard disks confined between hard lines to explore the relationship between the inherent structure landscape, the thermodynamics, and the dynamics of the fluid. The transfer matrix method is used to obtain an exact description of the landscape, equation of state, and provide a mapping of configurations of the equilibrium fluid to their local jammed structures. This allows us to follow how the system samples the landscape as a function of occupied volume fraction ϕ. Configurations of the ideal gas map to the maximum in the distribution of inherent structures, with a jamming volume fraction ϕ(J)(*), and sample more dense basins with increasing ϕ. This suggests jammed states with a density below ϕ(J)(*) are inaccessible from the equilibrium fluid. The configurational entropy of the fluid decreases rapidly at intermediate ϕ before plateauing at a low value and going to zero as the most dense packing is approached. This leads to the appearance of a maximum in both the isobaric heat capacity and the inherent structure pressure. We also show that the system exhibits a crossover from fragile to strong fluid behavior, located at the heat capacity maximum. Structural relaxation in the fragile fluid is shown to be controlled by the presence of high order saddle points caused by neighboring defects that are unstable with respect to jamming and spontaneously rearrange to form a stable local environment. In the strong fluid, the defect concentration is low so that defects do not interact and relaxation occurs through the hopping of isolated defects between stable local packing environments.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".