Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Flux‐regulated crystallization (FRC), a method that dynamically monitors and adjusts crystal growth from solutions in real time using computer vision and feedback control, has been recently introduced. Using FRC, centimeter‐scale perovskite single crystals at a linear growth rate of 0.2 mm h −1 with a standard deviation ( σ ) of 0.061 mm h −1 is synthesized. Here, machine learning is integrated into FRC to predict solvent evaporation rates during crystallization in real time, thus leading to an over threefold decrease in σ to 0.018 mm h −1 . This also results in improved reproducibility of perovskite crystallinity, as evidenced by average full width at half maximum of 22 ± 5 arcsec in X‐ray rocking curve measurements; and of perovskite X‐ray detectors, as evidenced by an average sensitivity of 4500 ± 500 µC Gy air −1 cm −2 at an electric field of 100 V cm −1 across 13 devices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it