Molecular dynamics methodology to investigate steady-state heterogeneous crystal growth
Bibliographic record
Abstract
In this paper a new molecular dynamics simulation methodology to investigate steady-state heterogeneous crystal growth from a supercooled liquid is presented. The method is tested on pure component systems such as Lennard-Jonesium and water/ice, as well as multicomponent systems such as methane hydrate crystals. The setup uses periodicity in all three directions and two interfaces; at one interface, crystallization occurs, while at the other, melting is enforced by locally heating the crystal only near that interface. Steady-state conditions are achieved when the crystal is melted at the same rate as the growth occurs. A self-adaptive scheme that automatically modifies the rate of melting to match the rate of growth, crucial for establishing steady-state conditions, is described. In contrast with the recently developed method of Razul et al. [Mol. Phys. 103, 1929 (2005)], where the rates of growth (melting) were constant and the temperatures determined, the present approach fixes the supercooling temperature at the growing interface and identifies the corresponding steady-state crystal growth rate that corresponds to the thermodynamic force provided. The static properties of the interface (e.g., the interfacial widths) and the kinetics of the crystal growth are found to reproduce well previous findings. The importance of establishing steady-state conditions in such investigations is also briefly discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".