Electrolyte Dependence of Li<sup>+</sup> Transport Mechanisms in Small Molecule Solvents from Classical Molecular Dynamics
Bibliographic record
Abstract
As demands on Li-ion battery performance increase, the need for electrolytes with high ionic conductivity and a high Li + transference number ( t Li ) becomes crucial to boost power density. Unfortunately, t Li in liquid electrolytes is typically <0.5 due to Li + migrating via a vehicular mechanism, whereby Li + diffuses along with its solvation shell, making its diffusivity slower than the counteranion. Designing liquid electrolytes where the Li + ion diffuses independently of its solvation shell is of significant interest to enhance the transference number. In this work, we elucidate how the properties of the solvent influence the Li + transport mechanism. Using classical molecular dynamics simulations, we find that a vehicular mechanism can be increasingly preferred with a decreasing solvent viscosity and increasing interaction energy between the solvent and Li + . Thus, a weaker interaction energy can enhance t Li through a solvent-exchange mechanism, ultimately improving Li-ion battery performance. Finally, metadynamics simulations show that in electrolytes where a solvent-exchange mechanism is preferable, the energy barrier to changing the coordination environment of Li + is much lower than in electrolytes where a vehicular mechanism dominates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".