Automated Perovskite Film Fabrication with High-Resolution Data Acquisition and Machine Learning Analysis

Abstract Lead halide perovskite solar cells have emerged as promising candidates for next-generation photovoltaics; nevertheless, fabrication of the perovskite photoactive layer remains highly intricate with film formation strongly modulated by various processing and environmental factors. Here, we report an environment-controlled, robot-assisted platform for automated thin-film fabrication designed to quantify and visualize perovskite crystallization in situ. Beyond precise control of each processing step, the system captures dynamic phenomena spanning subsecond color evolution and millisecond antisolvent droplet behavior while simultaneously recording slower environmental parameters (temperature, humidity/moisture, and concentrations of oxygen and volatile organic compounds). For optoelectronic assessment, we implemented time-resolved microwave conductivity (TRMC) measurements together with photoluminescence intensity quantification by using a complementary automated evaluation system. By combining machine-learning regression with SHAP (Shapley additive explanations) analysis, we identify the environmental parameters that most impact film properties and propose a feedback strategy to refine data and extract precise effects of process factors. A comparative study of a two-cation perovskite, (FAPbI3)0.87(MAPbBr3)0.13, and a three-cation perovskite, FA0.85MA0.10Cs0.05PbI3, further reveals markedly different sensitivities to environmental factors (FA, formamidinium; MA, methylammonium), in particular, temperature and resultant phase transition kinetics during thermal annealing. Based on the ML analysis and feedback, we tuned the antisolvent droplet velocity and obtained the maximized power conversion efficiency of 24.4%. Notably, this manual device fabrication was consistent with the results obtained by the automated system. This work establishes a practical route to stable, quantitative, and mechanistically informed investigation of solution-processed photovoltaic films.

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Publication Details

Journal
ACS Nano
Published
2026-10-27
DOI
https://doi.org/10.1021/acsnano.6c11386
Primary Topic
Perovskite Materials and Applications
Type
article
Field-Weighted Citation Impact
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article

Automated Perovskite Film Fabrication with High-Resolution Data Acquisition and Machine Learning Analysis

Fumitaka Ishiwari, Akinori Saeki, Ryosuke Nishikubo, Fumiya Iwai et al.
4 links
ACS Nano
Perovskite Materials and Applications
article

Automated Perovskite Film Fabrication with High-Resolution Data Acquisition and Machine Learning Analysis

Fumitaka Ishiwari, Akinori Saeki, Ryosuke Nishikubo, Fumiya Iwai, Ryosuke Kado
article en

Abstract

Abstract Lead halide perovskite solar cells have emerged as promising candidates for next-generation photovoltaics; nevertheless, fabrication of the perovskite photoactive layer remains highly intricate with film formation strongly modulated by various processing and environmental factors. Here, we report an environment-controlled, robot-assisted platform for automated thin-film fabrication designed to quantify and visualize perovskite crystallization in situ. Beyond precise control of each processing step, the system captures dynamic phenomena spanning subsecond color evolution and millisecond antisolvent droplet behavior while simultaneously recording slower environmental parameters (temperature, humidity/moisture, and concentrations of oxygen and volatile organic compounds). For optoelectronic assessment, we implemented time-resolved microwave conductivity (TRMC) measurements together with photoluminescence intensity quantification by using a complementary automated evaluation system. By combining machine-learning regression with SHAP (Shapley additive explanations) analysis, we identify the environmental parameters that most impact film properties and propose a feedback strategy to refine data and extract precise effects of process factors. A comparative study of a two-cation perovskite, (FAPbI3)0.87(MAPbBr3)0.13, and a three-cation perovskite, FA0.85MA0.10Cs0.05PbI3, further reveals markedly different sensitivities to environmental factors (FA, formamidinium; MA, methylammonium), in particular, temperature and resultant phase transition kinetics during thermal annealing. Based on the ML analysis and feedback, we tuned the antisolvent droplet velocity and obtained the maximized power conversion efficiency of 24.4%. Notably, this manual device fabrication was consistent with the results obtained by the automated system. This work establishes a practical route to stable, quantitative, and mechanistically informed investigation of solution-processed photovoltaic films.

ACS Nano
Tokyo Metropolitan University (JP), The University of Osaka (JP)
New Energy and Industrial Technology Development Organization, Japan Society for the Promotion of Science, Japan Science and Technology Agency
Affordable and clean energy
Openalex Percentile: Top 24%
Perovskite Materials and Applications
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