Magic number behavior for heat capacities of medium sized classical Lennard-Jones clusters
Bibliographic record
Abstract
Monte Carlo methods were used to calculate heat capacities as functions of temperature for classical atomic clusters of aggregate sizes $25 \\leq N \\leq 60$ that were bound by pairwise Lennard-Jones potentials. The parallel tempering method was used to overcome convergence difficulties due to quasiergodicity in the solid-liquid phase-change regions. All of the clusters studied had pronounced peaks in their heat capacity curves, most of which corresponded to their solid-liquid phase-change regions. The heat capacity peak height and location exhibited two general trends as functions of cluster size: for $N = 25$ to 36, the peak temperature slowly increased, while the peak height slowly decreased, disappearing by $N = 37$; for $N = 30$, a very small secondary peak at very low temperature emerged and quickly increased in size and temperature as $N$ increased, becoming the dominant peak by $N = 36$. Superimposed on these general trends were smaller fluctuations in the peak heights that corresponded to ``magic number'' behavior, with local maxima found at $N = 36, 39, 43, 46$ and 49, and the largest peak found at $N = 55$. These magic numbers were a subset of the magic numbers found for other cluster properties, and can be largely understood in terms of the clusters' underlying geometries. Further insights into the melting behavior of these clusters were obtained from quench studies and by examining rms bond length fluctuations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".