Journal Article

Unveiling the statistical behaviors of metal‐halide perovskites from films to devices through a high‐throughput experimental platform

Luo, Pingping, Deng, Can, Liu, Ziyi, Sun, Xiangyu, Li, Heng, Zhao, Yicheng, Yang, Liyi, Liu, Bowen, Lu, Xi, Tang, Liu, Song, Yushan

2026

Overview

Understanding the statistical behaviors from films to devices is crucial for performance prediction and materials innovation. Here, we present the first fully automated high‐throughput experimental platform for metal‐halide perovskite research in China, integrating solution preparation, film fabrication, electrode evaporation, and comprehensive optical/optoelectronic characterization. This platform enables human‐interference‐free data collection with high repeatability, facilitating reliable statistical analysis. Through systematic investigation of over 1000 perovskite samples, we first identify the key factor of solvent atmosphere affecting experimental repeatability, and then introduce a super‐absorbent resin to effectively mitigate solvent‐related variability. By quantitative tracking of statistical distributions across the film‐to‐device transformation, we reveal that the deposition of charge transport layers also alters the bulk properties of perovskite films, as manifested by statistical changes in bandgap and Urbach energy. Finally, we develop a machine learning‐based predictive model that links thin‐film optical features to device performance, demonstrating the feasibility of AI‐driven approaches to accelerate the evolution of perovskite materials. We have developed a fully automated high‐throughput experimental platform for electronic thin films and devices. Through statistical analysis, we demonstrated the transformative potential of combining high‐throughput experimentation with statistical methods to accelerate materials research, offering a new paradigm for material discovery and performance optimization.

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Charge deposition / Charge transport / Datasets / high‐throughput experimental platform / Machine learning / metal‐halide perovskite / Optoelectronics / Performance prediction / Perovskites / Prediction models / Reproducibility / Sample size / Software / Solvents / Spectrum analysis / Standard deviation / Statistical analysis / Statistical distributions / Thin films