Formulation of a Statistical Mechanical Theory To Understand the Li Ion Conduction in Crystalline Electrolytes: A Case Study on Li-Stuffed Garnets
Bibliographic record
Abstract
Ionic conductivity in solids is being computed using a wide range of computational methods such as molecular dynamics simulations and is measured using experimental methods, including electrochemical impedance spectroscopy and dc methods, and solid-state nuclear magnetic resonance spectroscopy. We report for the first time a statistical mechanical approach to estimate Li ion conductivity in the crystalline Li-stuffed garnet-type structure Li 5 La 3 Ta 2 O 12, Li 5.5 La 2.5 Ba 0.5 Ta 2 O 12, and Li 6 La 2 BaTa 2 O 12 . The estimated conductivity and activation energy for ionic conduction were found to be very consistent with experimental values for all three investigated garnets. The ionic conductivity was computed from the electrostatic friction coefficient of the Li ion using a combination of nonequilibrium statistical mechanics and electrostatics. The developed theory is derived from the fundamental transport equations that can be adapted to a wide range of crystalline ceramics electrolytes where crystallographic information is available, and sophisticated computational software and equipment may not be needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".