Semiautomated Experiments to Accelerate the Design of Advanced Battery Materials: Combining Speed, Low Cost, and Adaptability
Why this work is in the frame
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Bibliographic record
Abstract
A number of methodologies are currently being exploited in order to dramatically increase the composition space explored in the design of new battery materials. This is proving necessary as commercial Li-ion battery materials have become increasingly high-performing and complex. For example, commercial cathode materials have quinary compositions with a sixth element in the coating, while a very large number of contenders are still being considered for solid electrolytes, with most of the periodic table being at play. Furthermore, the promise of accelerated design by computation and machine learning (ML) are encouraging, but they both ultimately require large amounts of quality experimental data either to fill in holes left by the computations or to be used to improve the ML models. All of this leads researchers to increase experimental throughputs. This perspective focuses on semiautomated experimental approaches where automation is only utilized in key steps where absolutely necessary in order to overcome bottlenecks while minimizing costs. Such workflows are more widely accessible to research groups as compared to fully automated systems, such that the current perspective may be useful to a wide community. The most essential steps in automation are related to characterization, with X-ray diffraction being a key bottleneck. By analyzing published workflows of both semi- and fully automated workflows, it is found herein that steps handled by researchers during the synthesis are not prohibitive in terms of overall throughput and may lead to greater flexibility, making more synthesis routes possible. Examples will be provided in this perspective of workflows that have been optimized for anodes, cathodes, and electrolytes in Li batteries, the vast majority of which are also suitable for battery technologies beyond Li.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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