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Accelerating Perovskite Solar Cell Development Through High-Throughput Technologies: Computational Simulation, Automated Experimentation, and Intelligent Integration
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DOI：10.1002/bte2.70127.png)
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Abstract
En 中文
Perovskite solar cells have emerged as promising candidates for next-generation photovoltaics. However, optimizing their efficiency and long-term stability is complicated by the vast parameter space involving composition, processing, and device architecture, and so on. Conventional trial-and-error approaches are labor-intensive and often inefficient for navigating these complex multidimensional interactions. Consequently, the field is shifting toward high-throughput (HT) data-driven methodologies capable of accelerating the development cycle. This review highlights recent progress in HT computational and experimental strategies, along with their synergistic integration. Regarding computational efforts, we summarize the use of density functional theory and machine learning to identify single, double, and derivative perovskite candidates, as well as emerging techniques in literature data mining. We also discuss automated experimental platforms utilizing automatic processing and combinatorial characterization to optimize material compositions, device fabrication processes, and operational stability. Furthermore, we examine the integration of these domains through closed-loop workflows that combine computational prediction with automated experimentation to establish intelligent prediction, fabrication, and validation cycles. Finally, we provide an outlook on prevailing challenges regarding data acquisition, machine learning integration, HT platform infrastructure, and multi-modal data processing, alongside potential solutions for achieving fully autonomous material discovery and device optimization.
Keywords:
automated experimentation
high-throughput computation
machine learning
perovskite solar cell
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