The commercialization of perovskite solar cells (PSCs) is bottlenecked by inefficient trial-and-error approaches reliant on human expertise in both materials discovery and device fabrication1–3. Here we introduce an autonomous closed-loop framework that integrates machine learning (ML)-driven materials discovery with an automated manufacturing platform. The system uses active learning and quantum modelling to rapidly identify high-performance molecules and the platform uses Bayesian optimization and symbolic regression in a feedback loop to continuously refine the fabrication process. This integrated approach enabled the discovery of a passivation molecule, 5-(aminomethyl)nicotinonitrile hydroiodide (5ANI), which yielded 0.05-cm2 solar cells with a power conversion efficiency (PCE) of 27.22% (certified maximum power point tracking (MPPT) efficiency of 27.18%) and 21.4-cm2 mini-modules with a PCE of 23.49%. Moreover, the devices exhibited long-term operational stability, retaining 98.7% of their initial efficiency after 1,200 h of continuous operation under the ISOS-L-1I protocol. Crucially, the automated platform achieved an efficiency reproducibility nearly five times that of manual fabrication. This work establishes an automated closed-loop system that synergizes ML-powered discovery with the high-fidelity data from automated manufacturing, setting a benchmark for autonomous discovery and manufacturing in photovoltaics and materials.
