Does Local Structure Bias How a Crystal Nucleus Evolves?
Bibliographic record
Abstract
The broad scientific and technological importance of crystallization has led to significant research probing and rationalizing crystallization processes, particularly how nascent crystal phases appear. Previous work has generally neglected the possibility of the molecular-level dynamics of individual nuclei coupling to local structures (e.g., that of the nucleus and its surrounding environment). However, recent experimental work has conjectured that this can occur. Therefore, to address a deficiency in scientific understanding of crystallization, we have probed the nucleation of prototypical single and multi-component crystals (specifically, ice and mixed gas hydrates). Here, we establish that local structures can bias the evolution of nascent crystal phases on a nanosecond timescale by, for example, promoting the appearance or disappearance of specific crystal motifs, and thus reveal a new facet of crystallization behaviour. Analysis of the crystallization literature confirms that structural biases are likely present during crystallization processes beyond ice and gas hydrate formation. Moreover, we demonstrate that structurally-biased dynamics are a lens for understanding existing computational and experimental results while pointing to future opportunities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".