Communication—Design of LiNi <sub>0.2</sub> Mn <sub>0.2</sub> Co <sub>0.2</sub> Fe <sub>0.2</sub> Ti <sub>0.2</sub> O <sub>2</sub> as a High-Entropy Cathode for Lithium-Ion Batteries Guided by Machine Learning
Bibliographic record
Abstract
The use of “high-entropy” materials in electrodes is an emerging strategy to improve the stability and electrochemical properties of lithium-ion batteries. This study reports the machine learning-driven discovery of a high-entropy LiNi 0.2 Mn 0.2 Co 0.2 Fe 0.2 Ti 0.2 O 2 layered oxide cathode. Battery testing reveals a good initial capacity (160 mAh g −1 ) with exceptional stability up to 4.4 V. These materials are a promising way to expand the design space of cathode candidates while using inexpensive transition metals. However, further optimization of these materials is needed to improve battery performance relative to traditional cathodes.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".