The commercialization of perovskite solar cells is bottlenecked by inefficient, trial-and- error approaches reliant on human expertise in both material discovery and device fabrication (1-3). Here, we introduce an autonomous closed-loop framework that integrates machine learning (ML)-driven material discovery with an automated manufacturing platform. The system employs active learning and quantum modeling to rapidly identify high-performance molecules, while 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 hours of continuous operation under the ISOS-L-1I protocol. Crucially, the automated platform achieved an efficiency reproducibility nearly 5 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.
Gao et al. (Tue,) studied this question.