A comparative study of polyethylene oxide (PEO) using different coarse-graining methods
Bibliographic record
Abstract
Polyethylene oxide (PEO) holds significant importance in the field of batteries due to its high processability, intrinsic properties, and potential for high ionic conductivity. Achieving simulation at different scales is crucial for gaining a comprehensive understanding of its properties and thus improving them. In this context, we conducted a comparative study on the molecular physical structure, thermodynamic, and dynamic properties of PEO using three distinct coarse-grained (CG) procedures and all-atom (AA) simulations. The three CG simulation procedures involved modeling with MARTINI forcefield, SPICA forcefield, and an IBI derived potential from AA simulations. The AA simulation has been performed using the class 2 pcff+ forcefield. The ensuing simulated densities align significantly with the literature data, indicating the reliability of our approach. The solubility parameter from the AA simulation closely corresponds to literature reported values. MARTINI and SPICA yield almost similar solubility parameters, consistent with the similar density predicted by both the forcefields. Notably, SPICA forcefield closely reproduces the intermolecular structure of atomistic systems, as evidenced by radial distribution function (RDF). It also comprehensively replicates the distribution of radius of gyration (Rg) and the end-to-end distance (Re) of the atomistic samples. IBI ranks second to SPICA in emulating the structural properties of the atomistic systems, such as Rg, Re, and RDF. However, IBI falls short in accurately representing the solubility parameter of the amorphous PEO samples, while MARTINI does not provide an accurate representation of the structural properties of the systems. The use of SPICA forcefield results in enhanced dynamics of the systems in comparison with IBI and MARTINI.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".