The Vogel−Fulcher−Tamman Equation Investigated by Atomistic Simulation with Regard to the Adam−Gibbs Model
Bibliographic record
Abstract
Many efforts in experimental and theory are dedicated to study the puzzling problem of glass transition. With the involvement of molecular modeling, a fresh look at this intricate phenomenon was taken. Nevertheless, the difficulty to accurately probe all the domains of times necessary to describe this process still remains. Moreover, using a full-atomic description to account for equilibrated systems has been called into question. However, such depiction offers a special regard since it could deal with small modifications in the polymer architecture. From an appropriate selection of phase space, it was shown that atomistic simulation was able to link simulated and experimental glass transition temperatures of vinylic polymers using the established Williams−Landel−Ferry equation. Consequently, atomic insight into the glass transition phenomenon can be gained from a comparison of simulated data with actual models and experimental data. In this paper, the different parameters intervening in the Vogel−Fulcher−Tamman equation stemming from the local dynamics of a series of vinylic polymers are thus compared. Their behavior yields an atomistic viewpoint of the Adam−Gibbs model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".