Effects of long jumps, reversible aggregation, and Meyer-Neldel rule on submonolayer epitaxial growth
Bibliographic record
Abstract
We demonstrate, using kinetic Monte Carlo simulations of submonolayer epitaxial growth, that long jumps and reversible aggregation have a major impact on the evolution of island morphologies. Long jumps are responsible for a supra-Arrhenius behavior of the effective diffusion coefficient as the attachment and detachment kinetics give rise to a bimodal island size distribution that depends on temperature and long jump extent limits. As the islands density increases with temperature, the average size of stable islands reaches a maximum before decreasing. We have also observed that the diffusion coefficient cannot be used alone to predict the evolution of island sizes and morphologies, the relative rate of each process having a major importance. Our theoretical developments are of direct relevance for materials systems such as Au, Pd, Ag, Cu, Ni, H/Si , H/W(110), Co/Ru , and Co/Ru(S), that are known for exhibiting a compensation effect that cannot be contained within experimental uncertainties.
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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 | 0.001 |
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".