Challenges in analysing and visualizing large-scale molecular dynamics simulations: domain and defect formation in lung surfactant monolayers
Bibliographic record
Abstract
Molecular dynamics simulations have rapidly grown in size and complexity, as computers have become more powerful and molecular dynamics software more efficient. Using coarse-grained models like MARTINI system sizes of the order of 50 nm × 50 nm × 50 nm can be simulated on commodity clusters on microsecond time scales. For simulations of biological membranes and monolayers mimicking lung surfactant this enables large-scale transformation and complex mixtures of lipids and proteins. Here we use a simulation of a monolayer with three phospholipid components, cholesterol, lung surfactant proteins, water, and ions on a ten microsecond time scale to illustrate some current challenges in analysis. In the simulation, phase separation occurs followed by formation of a bilayer fold in which lipids and lung surfactant protein form a highly curved structure in the aqueous phase. We use Voronoi analysis to obtain detailed physical properties of the different components and phases, and calculate local mean and Gaussian curvatures of the bilayer fold.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".