ArticleAdvanced materials (Deerfield Beach, Fla.)2026
Synthesizer: Chemistry-Aware Machine Learning for Precision Control of Nanocrystal Growth.
Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Machine Learning-Driven Advances in Perovskite Materials and Solar Cells.Nanomaterials (Basel, Switzerland) · 2026Review
- Organic-Inorganic Metal Halide Perovskites: Toward Stability, Chirality, and AI-Guided Discovery.ACS central science · 2026Review
- Synthesizer: Chemistry-Aware Machine Learning for Precision Control of Nanocrystal Growth.Advanced materials (Deerfield Beach, Fla.) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Precise and reproducible control over nanocrystal synthesis is essential for tailoring optical properties, yet remains a long-standing challenge in halide perovskites. A broadly adoptable machine learning-guided framework, the Synthesizer, is introduced that combines Gaussian Process regression and Bayesian optimization with chemistry-aware molecular encodings and systematic feature engineering. Rather than new algorithms, the advance lies in translating interpretable machine learning tools into a practical, benchtop platform for nanocrystal optimization under ambient conditions. Using CsPbBr
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.