Study of lithium transport in Li2O component of the solid electrolyte interphase in lithium-ion batteries
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Bibliographic record
Abstract
In lithium-ion batteries (LIBs), as a promising energy storage device, materials with fast lithium (Li) transport are required for high-power applications such as electric vehicles. Thus, a deeper understanding of Li transport in components of LIBs is crucial for improving their rate capability. In this study, the Li transport in lithium oxide (Li2O), as one of the key components of the solid electrolyte interphase (SEI) layer, was examined by a multiscale computational approach ranging from density functional theory (DFT) to Monte Carlo simulations. The DFT calculations were used to investigate the recombination of Frenkel pairs, their first-principles total energies, and the Li diffusion mechanisms. The effect of atomic configurations on both first-principles total energies and diffusion barrier energies was formulated by periodic and local cluster expansions. These formalisms were then incorporated into the Monte Carlo and Kinetic Monte Carlo (KMC) simulations to calculate the diffusion coefficient of Li. Our calculations revealed that the vacancy-mediated jump along the 〈100〉 direction in the antifluorite structure of Li2O possesses the lowest barrier energy compared to other diffusion mechanisms. The KMC simulations indicated that the diffusion coefficient of Li better converged with the direct experimental measurement when the recombination of Frenkel pairs was integrated into the simulations. At a temperature of 300 K, the KMC simulation yielded a Li diffusion coefficient of 3.8×10-12cm2/s in Li2O. This is only one order of magnitude larger than indirect experimental measurement, suggesting the accuracy of our formalism. Thus, our formalism for studying Li transport in Li2O will pave the path to a rational design of inorganic SEI in the future development of LIBs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it