Stabilization of Na‐Ion Cathode Surfaces: Combinatorial Experiments with Insights from Machine Learning Models
Bibliographic record
Abstract
Na–Fe–Mn–O cathodes hold promise for environmentally benign high‐energy sodium‐ion batteries, addressing material scarcity concerns in Li‐ion batteries. To date, these materials show poor stability in the air and suffer significant Fe/Mn dissolution during use. These two detrimental surface effects have so far prevented the commercialization of these materials. Herein, high‐throughput experiments to make hundreds of substitutions into a previously optimized Na–Fe–Mn–O material are utilized. Numerous single‐phase materials are made with good electrochemical performance that shows moderate improvements over the unsubstituted. By contrast, dramatic improvements are made in suppressing decomposition in air and Fe/Mn dissolution. Machine learning algorithms are utilized to further understand the changes in air stability and to decouple the effects of various structural parameters such as lattice parameters and crystallite size. The comprehensive dataset and methodology established here lay the groundwork for future exploration and optimization of cathode materials, driving the advancement of next‐generation sodium‐ion batteries.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".