Comprehensive Dopant Screening in Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> Garnet Solid Electrolyte
Bibliographic record
Abstract
Abstract Promising Li7La3Zr2O12 (LLZO) garnet electrolytes for solid Li batteries are highly sensitive to doping to modify performance. Herein, LLZO samples with 59 different elemental dopants are synthesized with substitutions on each of the 3 sites (177 total materials). Many potential dopants successfully integrate into the LLZO garnet crystal structure (either cubic or tetragonal), while doping on the optimum site predicted from either previous DFT calculations or the far cheaper bond valence calculations promotes the cubic phase. Room temperature ionic conductivities of up to 1.2 × 10−3 S cm−1 are achieved demonstrating the quality of materials made in high‐throughput here, and 36 different dopants yield a >10x improvement in conductivity over undoped LLZO. This opens up dramatically the playground for new garnet materials. Other important metrics for electrolytes are also screened systematically. Electronic conductivity is generally suppressed with doping, though certain dopants need to be avoided as they enhance the risk of dendrite formation. The electrochemical stability window of the doped LLZO samples is also screened carefully and shows tunability with certain dopants improving the high voltage stability while others help at low voltage. The results will therefore serve to guide rational codoping studies to combine the benefits of various dopants.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".