Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

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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Publication Details

Journal
Advanced Functional Materials
Published
2025-04-17
DOI
https://doi.org/10.1002/adfm.202506188
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.04

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article

Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

Yuki Haruta, Magdalena Bazalova‐Carter, Alexander Hart, Makhsud I. Saidaminov et al.
2 citations
Advanced Functional Materials
Machine Learning in Materials Science
1.04
article

Flux‐Regulated Crystallization of Perovskites Using Machine Learning‐Predicted Solvent Evaporation Rates for X‐Ray Detectors

Yuki Haruta, Magdalena Bazalova‐Carter, Alexander Hart, Makhsud I. Saidaminov, Marcos J. L. Santos, Tatiane Pretto, Sergey V. Dayneko, Antoine Pavesic, Nicholas Sandor
article en
2 citations

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.

Advanced Functional MaterialsVol. 36(21)
Universidade Federal do Rio Grande do Sul (BR), University of Victoria (CA), Advanced Materials and Technologies (Slovenia) (SI)
Canada Research Chairs, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Agência Nacional do Petróleo, Gás Natural e Biocombustíveis, Natural Sciences and Engineering Research Council of Canada, Division of Graduate Education
Openalex Percentile: Top 26%
Machine Learning in Materials Science
1.04
DOIOpen Access PDFOpenAlex
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