High-Throughput Evaluation of Emission and Structure in Reduced-Dimensional Perovskites
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide High-throughput experimentation (HTE) seeks to accelerate the exploration of materials space by uniting robotics, combinatorial methods, and parallel processing. HTE is particularly relevant to metal halide perovskites (MHPs), a diverse class of optoelectronic materials with a large chemical space. Here we develop an HTE workflow to synthesize and characterize light-emitting MHP single crystals, allowing us to generate the first reported data set of experimentally derived photoluminescence spectra for low-dimensional MHPs. We leverage the accelerated workflow to optimize the synthesis and emission of a new MHP, methoxy-phenethylammonium lead iodide ((4-MeO-PEAI) 2 -PbI 2 ). We then synthesize 16 000 MHP single crystals and measure their photoluminescence to study the effects of synthesis parameters and compositional engineering on the emission intensity of 54 distinct MHPs: we achieve an acceleration factor of more than 100 times over previously reported HTE MHP synthesis and characterization methods. Using insights derived from this analysis, we screen an existing database for new, potentially emissive MHPs. On the basis of the Tanimoto similarity of the bright available emitters, we present our top candidates for future exploration. As a proof of concept, we use one of these (3,4-difluorophenylmethanamine) to synthesize an MHP which we find has a photoluminescence quantum yield of 10%.
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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".