Autonomous closed-loop framework for reproducible perovskite solar cells

"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. This highlights the need for innovative solutions to enhance efficiency."

"We introduce an autonomous closed-loop framework that integrates machine learning-driven material discovery with an automated manufacturing platform, addressing the limitations of traditional methods."

"The system employs active learning and quantum modeling to rapidly identify high-performance molecules, showcasing the potential of machine learning in advancing solar cell technology."

"This integrated approach enabled the discovery of a passivation molecule, 5-(aminomethyl)nicotinonitrile hydroiodide (5ANI), which yielded significant improvements in solar cell power conversion efficiency."

The commercialization of perovskite solar cells faces challenges due to inefficient methods reliant on human expertise. An autonomous closed-loop framework is introduced, combining machine learning-driven material discovery with automated manufacturing. This system utilizes active learning and quantum modeling to identify high-performance molecules quickly. Additionally, Bayesian optimization and symbolic regression are employed in a feedback loop to refine the fabrication process continuously. This approach led to the discovery of a passivation molecule, 5-(aminomethyl)nicotinonitrile hydroiodide (5ANI), resulting in improved solar cell performance.

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