Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".