Estimation of linear, ring, and star polyethylene viscosity through proper orthogonal decomposition and <scp>Voronoi</scp> tessellation analysis of molecular dynamics data
Bibliographic record
Abstract
Abstract We carried out molecular dynamics (MD) simulations on polyethylene (PE) with linear, ring, and four‐arm symmetrical star structures at various molecular weights (420 − 5700 g ⋅ mol −1 ) and temperatures (410−450 K). We then analyzed the MD data using the technique of proper orthogonal decomposition (POD) to obtain the time correlation functions of different eigenmodes, thereby calculating the viscosity of the aforementioned macromolecules. The time correlation functions show that the ring and star PEs relax much faster than their linear counterpart. Free volume size distributions and the mean fractional free volume of the equilibrated PE melts were determined by Voronoi tessellation (VT). The mean fractional free volume and the corresponding effective pressure were then used to calculate viscosity using a polymer free volume theory recently developed in our lab. The POD and VT approaches yielded similar viscosity values. Furthermore, they both successfully predicted the crossover in the molecular weight dependence of viscosity. It is interesting to note that the difference in the free volume parameters ( ϕ + − F ) of various structures always fall within the range of 0.02−0.06. Here, F and ϕ + signify the probability of a bead having enough free volume for the activation of diffusive motion or momentum transfer and the minimum required fraction of such beads, respectively. The temperature dependence of viscosity as obtained from the POD and VT approaches gave comparable apparent activation energy ( ) values of 5.8−6.4 and 5.4−6.4 kcal ⋅ mol −1 , respectively, for the three structures.
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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 | 0.000 | 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 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".