Unconstrained Machine Learning Screening for New Li‐Ion Cathode Materials Enhanced by Class Balancing
Bibliographic record
Abstract
Abstract Modeling and predicting battery cathode material voltage requires accurate structural information regarding the binding sites of lithium within a target structure. Obtaining these optimized structures requires some form of structural optimization. The ensuing complexity impedes the rapid screening of new materials for their suitability in energy storage. Previous machine learning (ML) models use structures of both lithiated and nonlithiated forms for training; essentially, reproducing what is already known, but failing to generalize to structures whose lithiated form is not available. To avoid this limitation, an ML model capable of predicting the voltage associated with the material's lithiation without explicitly requiring the lithiated structure is trained. The model's predictive power is improved by adding newly calculated data points, with the most impactful being materials with unfavorable Li binding, which are lacking in the original dataset. Using this model, new cathode candidates among an order of magnitude more materials than in previous studies are screened and the most promising ones are validated with density functional theory calculations. Considering additional stability and conductivity constraints, 572 materials with voltages greater than 3.5 V are predicted. Unexpectedly, some of them are not based on conventional transition metals, highlighting the power of an unbiased search.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".