Simulation of short-chain polymer collapse with an explicit solvent
Bibliographic record
Abstract
We study the equilibrium behavior and dynamics of a polymer collapse transition for a system composed of a short Lennard-Jones (LJ) chain immersed in a LJ solvent for solvent densities in the range of ρ=0.6–0.9 (in LJ reduced units). The monomer hydrophobicity is quantified by a parameter λ∈[0,1] which gives a measure of the strength of attraction between the monomers and solvent particles, and which is given by λ=0 for a purely repulsive interaction and λ=1 for a standard LJ interaction. A transition from the Flory coil to a molten globule is induced by increasing λ. Generally, the polymer size decreases with increasing solvent density for all λ. Polymer collapse is induced by changing the hydrophobicity parameter from λ=0 to λ⩾0.5, where the polymer is in a molten globule state. The collapse rate increases monotonically with increasing hydrophobicity and decreases monotonically with increasing solvent density. Doubling the length of the chain from N=20 to N=40 monomers increases the collapse time roughly by a factor of 2, more or less independent of the hydrophobicity and solvent density. We also study the effect of conformational restrictions on polymer collapse using a chain model in which the bond angles are held near 109.5° using a stiff angular harmonic potential, but where free internal rotation is allowed, and find that the collapse times increase considerably with respect to the fully flexible polymer, roughly by a factor of 1.6–3.5. This increase is most pronounced for high solvent densities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".