United-Atom Molecular Dynamics Study of the Mechanical and Thermomechanical Properties of an Industrial Epoxy
Bibliographic record
Abstract
Epoxy resins are the most commonly used adhesives in industry due to their versatility, low cost, low toxicity, low shrinkage, high strength, resistance to moisture, and effective electrical resistance. These diverse properties can be tailored based on the chemical structure of the curing agent and the conditions of the curing process. Molecular simulations of epoxy resins have gained attention in recent years as a means to navigate the vast choice of chemical agents and conditions that will give the required properties of the resin. This work examines the statistical uncertainty in predicting thermodynamic and mechanical properties of an industrial epoxy resin using united atom molecular dynamics simulation. The results are compared with experimental measurements of the elastic modulus, Poisson's ratio, and the glass transition temperature obtained at different temperatures and degrees of curing. The decreasing trend of the elastic modulus with increasing temperature is accurately captured by the simulated model, though the uncertainty in the calculated average is large. The glass transition temperature is expectedly overpredicted due to the high rates accessible to molecular simulations. We find that Poisson's ratio is particularly sensitive to sample anisotropy as well as the method of evaluation, which explains the lack of consistent trends previously observed with molecular simulation at different degrees of crosslinking and temperatures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".