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.
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