Combinatorial methods in advanced battery materials design
Bibliographic record
Abstract
In the search for better performing battery materials, researchers have increasingly ventured into complex composition spaces, including numerous pseudo-quaternaries, with further substitutions being either explored experimentally or proposed based on computation. Given the vast composition spaces that need exploring, experimental combinatorial science can play an important role in accelerating the development of advanced battery materials and is arguably the best means to obtain a sufficiently large data set to truly bring a high degree of precision to advanced computational techniques such as machine-learning. Herein, we present a robust high-throughput synthesis platform that is currently being used in the McCalla lab at McGill University to study Li-ion cathodes, anodes, and solid electrolytes, as well as Na-ion cathodes. The synthesis methods used are presented in detail, as are the high-throughput characterization techniques we utilize regularly (X-ray diffraction, electrochemical testing, and electrochemical impedance spectroscopy). We quantitatively determine the high precision and reproducibility achieved by this combinatorial system and also demonstrate its versatility by presenting for the first time combinatorial data for two high-power anodes for Li-ion batteries (TiNb2O7 and W3Nb14O44), as well as solid state electrolyte Li7La3Zr2O12. Our methods reproduce accurately the results from the literature for bulk samples, indicating that the high-throughput methodology utilizing small milligram-scale samples scales up extremely well to the larger sample sizes typically used in both the literature and industry. The throughput of this combinatorial infrastructure has a current limit of 896 XRD patterns and 896 EIS patterns a week and 448 cyclic voltammograms running simultaneously.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".