Mechanisms of heterogeneous crystal growth in atomic systems: Insights from computer simulations
Bibliographic record
Abstract
In this paper we analyze the atomic-level structure of solid/liquid interfaces of Lennard-Jones fcc systems. The 001, 011, and 111 faces are examined during steady-state growth and melting of these crystals. The mechanisms of crystallization and melting are explored using averaged configurations generated during these steady-state runs, where subsequent tagging and labeling of particles at the interface provide many insights into the detailed atomic behavior at the freezing and melting interfaces. The interfaces are generally found to be rough and we observe the structure of freezing and melting interfaces to be very similar. Large structural fluctuations with solidlike and liquidlike characteristics are apparent in both the freezing and melting interfaces. The behavior at the interface observed under either growth or melting conditions reflects a competition between ordering and disordering processes. In addition, we observe atom hopping that imparts liquidlike characteristics to the solid side of the interfaces for all three crystal faces. Solid order is observed to extend as rough, three-dimensional protuberances through the interface, particularly for the 001 and 011 faces. We are also able to reconcile our different measures for the interfacial width and address the onset of asymmetry in the growth rates at high rates of crystal growth/melting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".