New Simulation Model of Multicomponent Crystal Growth And Inhibition
Bibliographic record
Abstract
We review a novel computational model for the study of crystal structures both on their own and in conjunction with inhibitor molecules. The model advances existing Monte Carlo (MC) simulation techniques by extending them from modeling 3D crystal surface patches to modeling entire 3D crystals, and by including the use of "complex" multicomponent molecules within the simulations. These advances makes it possible to incorporate the 3D shape and non-uniform surface properties of inhibitors into simulations, and to study what effect these inhibitor properties have on the growth of whole crystals containing up to tens of millions of molecules. The application of this extended MC model to the study of antifreeze proteins (AFPs) and their effects on ice formation is reported, including the success of the technique in achieving AFP-induced ice-growth inhibition with concurrent changes to ice morphology that mimic experimental results. Simulations of ice-growth inhibition suggest that the degree of inhibition afforded by an AFP is a function of its ice-binding position relative to the underlying anisotropic growth pattern of ice. This extended MC technique is applicable to other crystal and crystal-inhibitor systems, including more complex crystal systems such as clathrates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".