Study of Lithium Transport in NMC Layered Oxide Cathode Material Using Multiscale Computational Approach
Bibliographic record
Abstract
Enhancing the rate capability of lithium-ion batteries (LIBs), as a promising energy storage device, requires a comprehensive understanding of lithium (Li) transport in their constituent parts. In this study, Li transport in the LiNi 0.333 Mn 0.333 Co 0.333 O 2 (NMC111) cathode active material was examined by a multiscale computational approach ranging from density functional theory (DFT) to Monte Carlo (MC) simulations. The approach was first applied to lithium cobalt oxide (LCO) to compare our model with an existing available one for barrier energies in layered structures. Two barrier energy models, named the interpolated barrier model and the local cluster expansion, together with the periodic cluster expansion, were integrated into the KMC algorithm. Results of KMC simulations in LCO were similar using both barrier models. Thus, the approach was then applied to NMC111 by using only the much simpler interpolated barrier model. Our MC simulations showed a perfect honeycomb-like ordering of Li ions in the Li layer of NMC111 at a Li concentration of 0.8. This perfect ordering of Li ions caused a significant decrease in the thermodynamic factor, which consequently resulted in a minimum in the chemical diffusion coefficient at this concentration, confirming previous studies. The perfect correlation between our simulations and the experimental measurements of other studies reflects the precision of our formalism in studying the transport behavior of Li in the NMC111 crystal.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".