High-throughput exploration of halide perovskite compositionally-graded films and degradation mechanisms
Bibliographic record
Abstract
Abstract The conventional approach to search for new materials is to synthesize a limited number of candidates. However, this approach might delay or prevent the discovery of better-performing materials due to the narrow composition space explored. Here, we fabricate binary alloy films with a composition gradient in a single shot in less than one minute. We apply this approach to study the stability of halide perovskites. We synthesize all possible binary compositions from MAPbI3 and MAPbBr3 and then study their optical properties, structure, and environmental stability in a high-throughput manner. We find that perovskite alloys experience three different degradation mechanisms depending on halogen content: bromine-rich perovskites degrade by hydration, iodine-rich perovskites by the loss of the organic component, and all other intermediate alloys by phase segregation. The proposed method offers an avenue for discovering new materials and processing parameters for a wide range of applications that rely on compositional engineering.
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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".