Automatic migration path exploration for multivalent battery cathodes using geometrical descriptors
Why this work is in the frame
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Bibliographic record
Abstract
Stable and fast ionic conductors for magnesium cathode materials have the prospect of enabling high energy density batteries beyond current Lithium-ion technologies. So far, only a few candidate materials have been identified leading to data only being scarcely available to the community. Here, we present a systematic study, in the framework of Density Functional Theory, including the estimation of the migration barrier for 16 materials through employing Nudged Elastic Band (NEB) calculations. By introducing a path finder algorithm based on the idea of Voronoi tessellations, we show that an estimate of the transition state configuration can be extracted automatically prior to running NEB-calculations. Using geometrical descriptors in combination with a principal component analysis it is possible to further sub-group the migration paths. This approach also extends to materials which are not part of the study, making it a viable approach to more efficiently explore crystal structures with distinguishable migration characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 it