A Critical Evaluation of the Advanced Electrolyte Model
Bibliographic record
Abstract
A fast and accurate method to obtain transport properties of electrolyte solutions for Li-ion batteries is of great interest for both screening potential electrolyte candidates and for use in physics-based models of Li-ion cells. The Advanced Electrolyte Model (AEM) considers various molecular-scale interactions in a chemical physics framework to calculate these electrolyte transport properties in a computationally inexpensive manner. Should these calculations match experiment well, the AEM would be an ideal tool for the rapid determination of transport properties for various electrolyte systems. This paper aims to evaluate the accuracy of the AEM against experimental viscosity and conductivity data for electrolytes of interest in lithium batteries. Recent measurements, as well as previous measurements of now-obsolete electrolyte systems, are compared to corresponding calculations from the AEM. The availability of accurate laboratory data has allowed for improved accuracy of the AEM theory, molecular parameters and related predictions of properties, in particular for certain systems with low concentrations of ethylene carbonate (i.e. low permittivity electrolytes), as well as systems containing the salt Li triflate or the solvent sulfolane. The model now provides accurate calculations for the transport properties of most of the different systems considered here.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".