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Related Experiment Video

Updated: Jan 10, 2026

Low Pressure Vapor-assisted Solution Process for Tunable Band Gap Pinhole-free Methylammonium Lead Halide Perovskite Films
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Spatial-Uniformity-Driven Bayesian Optimization for Rapid Development of Printed Perovskite Solar Cells.

Julia E Huddy1, Yanan Li1, Mariia Klymenko1

  • 1Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.

Small (Weinheim an Der Bergstrasse, Germany)
|November 26, 2025
PubMed
Summary

Bayesian optimization improves printed perovskite solar cells by focusing on spatial uniformity. This machine learning approach enhances large-area device performance and manufacturing efficiency.

Keywords:
bayesian optimizationflexographic printingperovskite solar cellsphotovoltaicsscalable coating

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Area of Science:

  • Materials Science
  • Optoelectronics
  • Machine Learning Applications

Background:

  • Printed metal halide perovskites offer cost-effective, high-throughput manufacturing for optoelectronics like solar cells.
  • Spatial heterogeneity in perovskite films hinders the performance of large-area devices despite current patterning capabilities.

Purpose of the Study:

  • To accelerate the development of printed perovskite solar cells using a spatial-uniformity-driven Bayesian optimization (BO) approach.
  • To enhance the performance of large-area perovskite solar cells by mitigating spatial heterogeneity.

Main Methods:

  • Leveraged a Bayesian optimization (BO) surrogate model to explore a 6D design space of ink chemistry and printing physics.
  • Utilized iterative experimentation (≈100 trials) guided by an objective function measuring spatial photoluminescence (PL) variance.
  • Performed rheological comparisons of ink formulations to address Saffman-Taylor artifacts and improve film uniformity.

Main Results:

  • Optimizing for uniformity significantly improved photovoltaic performance.
  • Achieved ≈20% power conversion efficiency (PCE) in small-area (0.134 cm²) devices.
  • Demonstrated >16% PCE in large-area (1 cm²) devices, showcasing scalability.

Conclusions:

  • Uniformity-driven BO is an efficient method for optimizing printed perovskite solar cells.
  • This approach effectively identifies key printing physics and mitigates spatial heterogeneity for scalable devices.
  • The machine learning strategy accelerates the development and improves the performance of perovskite-based optoelectronics.