Data-Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing

Abstract

The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing, particularly by better controlling multiple high-dimensional process parameters. This study proposes a machine learning (ML) approach to efficiently predict and analyze perovskite film fabrication processes. By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters, the Random Forest (RF) model was identified as the most effective, enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation. The RF model demonstrated strong predictive accuracy, with an R² close to 0.8. This approach led to the identification of optimal process parameter combinations, significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold, thereby advancing the development of scalable manufacturing processes.