Hopping Rate and Migration Entropy as the Origin of Superionic Conduction within Solid-State Electrolytes
Bibliographic record
Abstract
Inorganic solid-state electrolytes (SSEs) have gained significant attention for their potential use in high-energy solid-state batteries. However, there is a lack of understanding of the underlying mechanisms of fast ion conduction in SSEs. Here, we clarify the critical parameters that influence ion conductivity in SSEs through a combined analysis approach that examines several representative SSEs (Li 3 YCl 6, Li 3 HoCl 6, and Li 6 PS 5 Cl), which are further verified in the x LiCl-InCl 3 system. The scaling analysis on conductivity spectra allowed the decoupled influences of mobile carrier concentration and hopping rate on ionic conductivity. Although the carrier concentration varied with temperature, the change alone cannot lead to the several orders of magnitude difference in conductivity. Instead, the hopping rate and the ionic conductivity present the same trend with the temperature change. Migration entropy, which arises from lattice vibrations of the jumping atoms from the initial sites to the saddle sites, is also proven to play a significant role in fast Li + migration. The findings suggest that the multiple dependent variables such as the Li + hopping frequency and migration energy are also responsible for the ionic conduction behavior within SSEs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".