Development of coarse-grained force field to investigate sodium-ion transport mechanisms in cyanoborate-based ionic liquid
Bibliographic record
Abstract
Sodium-ion batteries have a great potential for energy storage applications, however, its realization depends strongly on a deep atomistic understanding of the sodium-ion transport mechanisms, which is a key step to optimize its performance. A complete characterization of sodium-ion transport mechanism requires large time-scale calculations, which is a challenge even for classical atomistic force-field molecular dynamics simulations. Thus, in this work, we performed a calibration of a coarse-grained force-field to describe the thermodynamic and transport properties of sodium-ion for the particular case of cyanoborate-based ionic liquid using different molar fractions of sodium tetracyanoborate salt dissolved in 1-ethyl-3-methyl-imidazolium tetracyanoborate. The results from coarse-grained modeling were found to be in fair agreement with atomistic simulations and experimental data, and they captured concentration dependence of [Nax[Anion]y]x−y aggregate sizes. In particular, two distinct populations of sodium-ions were identified inside the [Nax[Anion]y]x−y aggregates: 1) a slow one in vehicular diffusion and 2) a fast one in hoping diffusion. Both of them contribute to sodium ion transport and occur preferentially at different sodium-ion concentrations. The present findings were obtained for a cyanoborate-based ionic liquid, however, we expect that our insights can be used to understand similar ionic liquids.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".