Optical Density of Perovskite Films as a Novel Parameter in Statistical Modeling for Perovskite Solar Cells

The selection of precursors, solvents and additives, as well as the optimization of deposition techniques, is crucial in the research and development of perovskite solar cells (PSCs). However, understanding how these variables affect material properties is an ongoing challenge. For this reason, we statistically analyze data acquired during the fabrication of blade‐coated perovskite films using data science (DS) and machine learning (ML) tools to identify relationships and trends across a high‐dimensional design space. A compact descriptor extracted from ultraviolet visible (UV–vis) spectroscopy absorption measurements is introduced to simplify high‐dimensional optical density ( OD ) curve data into a set of five numerical values defined for different wavelength intervals. This representation allows a quantitative assessment of optical absorbance that combined with film thickness, provides a reliable metric to monitor process–property relationships. The study explores synthesis and coating variables such as coater design, coating speed, drying process parameters (distance, nozzle angle, and pressure), solvents, and additive incorporation as the most relevant variables affecting thickness and OD . Therefore, the analysis of these variables, combined with a dimensionality reduction method, becomes a viable and promising methodology to investigate process‐property‐performance relationships, supporting their future application as light absorber layers in high‐performance PSCs.