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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| 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