The dependence on trial-and-error approaches and human expertise is a persistent barrier in the commercialization of perovskite photovoltaics, making materials discovery and device fabrication inefficient and hard to reproduce. Now, writing in Nature, Samuel Stranks, Xiao Cheng Zeng, Zonglong Zhu and collaborators demonstrate a fully integrated, closed-loop system that links machine-learning-guided molecular design with robotic device fabrication, enabling both the discovery of new passivation molecules and the reproducible manufacture of high-efficiency perovskite solar cells.