Simulation of heteropolymer collapse with an explicit solvent in two dimensions
Bibliographic record
Abstract
Molecular dynamics simulations are used to study the equilibrium properties and collapse dynamics of a heteropolymer in the presence of an explicit solvent in two dimensions. The system consists of a single copolymer chain composed of hydrophobic (H) and hydrophilic (P) monomers, immersed in a Lennard-Jones solvent. We consider HP chains of varying hydrophobic number fraction nH, defined as the ratio of the number of H monomers to the total number of monomers. We also consider homopolymer chains with a uniform variable degree of hydrophobicity λ, which describes the hydrophobic-solvent interaction, and which ranges from hydrophilic (λ=0) to hydrophobic (λ=1). We investigate the effects of varying nH and λ, the HP sequencing, and the solvent density on the equilibrium and collapse properties of the chain. For sufficiently high nH, we observe a collapse transition for random copolymers from a stretched coil to a liquidlike globule upon a decrease in temperature; the transition temperature decreases with increasing nH. The transition can also be induced at a fixed (and sufficiently low) temperature by varying nH for random copolymers or λ for homopolymers. We find that polymer size varies inversely with solvent density. The rate of polymer collapse is found to strongly vary inversely with increasing nH and λ for copolymers and homopolymers, respectively. Further, the collapse rates for these two cases are very close for nH=λ, except at lower values (nH=λ≈0.5), where the homopolymers collapse more rapidly. At moderate densities (ρ=0.5–0.7, in LJ reduced units), we find that random copolymers collapse more rapidly at low density and that this difference tends to increase with decreasing nH. At fixed solvent density and nH we find the collapse rate differs little for random copolymers, and multi-block copolymers with equal nH. Finally, the simulations suggest that copolymers tend to collapse by a uniform thickening rather than by first forming locally collapsed clusters which aggregate at longer time. The exception to this appears to be block-copolymers comprised of sufficiently long alternating H and P blocks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".