A self-driving lab for discovering tunable and soluble organic lasers
Bibliographic record
Abstract
We have recently demonstrated the ability of using self-driving laboratories for AI-driven searches of new emitters for organic solid-state lasing devices. Our past workflow featured solubility challenges for such large molecular moieties. In this next-generation study, we returned to the drawing board to explore a family of compounds that were much solution processable and composed of a set of electronic cores that would give a broader color response. Out of 252 potential candidates, we selectively performed a comprehensive study exploring 51 fluorene-based A-B-A type organic laser oligomers, armed with our self-driving lab. The candidates ranged from simple hydrocarbon molecules to complex hetero atom-mixed molecules. As a result of this study, we highlight diketopyrrolopyrrole and benzodiazole derivatives for largely red-shifted emissions. Furthermore, we investigated the effect of color change from hetero atom permutation, fluorine addition, thiophene coupling, and a combination of fluorine addition and thiophene coupling. The computational study with density functional theory confirmed the experimental results.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".