GoKawiilThe 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.
Autonomous closed-loop framework for reproducible perovskite solar cells
Why This Matters
This innovative autonomous framework accelerates the discovery and manufacturing of high-efficiency, stable perovskite solar cells, significantly reducing reliance on manual trial-and-error methods. By integrating machine learning with automated fabrication, it enhances reproducibility and speeds up the development process, potentially transforming photovoltaic manufacturing and material research. This advancement paves the way for more reliable, scalable, and efficient solar energy solutions, benefiting both industry and consumers.
Key Takeaways
- Automated system achieves nearly 5x higher reproducibility than manual methods.
- Machine learning accelerates the discovery of high-performance materials like 5ANI.
- Long-term stability of solar cells is demonstrated with 98.7% efficiency retention after 1,200 hours.
Explore topics:
perovskite solar cells
machine learning
bayesian optimization
5-(aminomethyl)nicotinonitrile
photovoltaics
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