High‐Throughput Exploration of Triple‐Cation Perovskites via All‐in‐One Compositionally‐Graded Films
Bibliographic record
Abstract
Abstract Many devices heavily rely on combinatorial material optimization. However, new material alloys are classically developed by studying only a fraction of giant chemical space, while many intermediate compositions remain unmade in light of the lack of methods to synthesize gapless material libraries. Here report a high‐throughput all‐in‐one material platform to obtain and study compositionally‐tunable alloys from solution is reported. This strategy is applied to make all CsxMAyFAzPbI3 perovskite alloys (MA and FA stand for methylammonium and formamidinium, respectively), in less than 10 min, on a single film, on which 520 unique alloys are then studied. Through stability mapping of all these alloys in air supersaturated with moisture, a range of targeted perovskites are found, which are then chosen to make efficient and stable solar cells in relaxed fabrication conditions, in ambient air. This all‐in‐one platform provides access to an unprecedented library of compositional space with no unmade alloys, and hence aids in a comprehensive accelerated discovery of efficient energy materials.
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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) | 0.002 | 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".