Bibliographic record
Abstract
The transition toward renewable energy sources is motivating a great deal of research into new secondary battery materials for important applications such as electric vehicles and grid storage. This research is generally very fragmented with each study only looking at a few compositions and each research team using their own methods/protocols, which greatly limits comparisons between studies. Recently, high-throughput methods have been developed and used to screen the impact of a very high number of dopants simultaneously (72 different elements at the latest count). These studies have focused on both electrodes in Li-ion batteries, Na-ion cathodes, and solid electrolytes for both Li and Na batteries. This large-data-driven research is highly efficient in generating advanced materials for practical devices, but it also provides a great opportunity to enhance our understanding of how substitutions impact the wide variety of intrinsic properties of importance for battery materials. Here, I summarize the key trends seen across these studies and provide a perspective of where this research is leading. This will include a discussion of progress toward a global understanding of how to predict whether a dopant will in fact dope into a structure (this has to date been poorly predicted by computational approaches) and also the potential to develop codoping strategies to optimize multiple key properties at once. Finally, opportunities to make use of these large data sets with artificial intelligence/machine-learning will be discussed. These will dramatically enhance our ability to rationally design advanced battery materials without limiting open exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".