As a pioneer of the third-generation photovoltaic revolution, Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential. The development process of PSCs is precise and complex, involving a series of closed-loop workflows such as literature retrieval, data integration, experimental design, and synthesis. However, existing AI perovskite approaches focus predominantly on discrete models, including material design, process optimization,and property prediction. These models fail to propagate physical constraints across the workflow, hindering end-to-end optimization. In this paper, we propose a multi-agent system for perovskite material discovery, named PeroMAS. We first encapsulated a series of perovskite-specific tools into Model Context Protocols (MCPs). By planning and invoking these tools, PeroMAS can design perovskite materials under multi-objective constraints, covering the entire process from literature retrieval and data extraction to property prediction and mechanism analysis. Furthermore, we construct an evaluation benchmark by perovskite human experts to assess this multi-agent system. Results demonstrate that, compared to single Large Language Model (LLM) or traditional search strategies, our system significantly enhances discovery efficiency. It successfully identified candidate materials satisfying multi-objective constraints. Notably, we verify PeroMAS's effectiveness in the physical world through real synthesis experiments.

翻译：作为第三代光伏革命的先驱，钙钛矿太阳能电池以其卓越的光电性能和成本潜力而著称。其开发过程精密且复杂，涉及文献检索、数据整合、实验设计与合成等一系列闭环工作流。然而，现有的人工智能钙钛矿研究方法主要集中于离散模型，包括材料设计、工艺优化和性能预测。这些模型无法在工作流中传递物理约束，阻碍了端到端的优化。本文提出了一种用于钙钛矿材料发现的多智能体系统，命名为PeroMAS。我们首先将一系列钙钛矿专用工具封装为模型上下文协议。通过规划和调用这些工具，PeroMAS能够在多目标约束下设计钙钛矿材料，涵盖从文献检索、数据提取到性能预测与机理分析的全过程。此外，我们构建了由钙钛矿领域专家参与的评估基准，以评估该多智能体系统。结果表明，与单一大型语言模型或传统搜索策略相比，我们的系统显著提升了发现效率，成功识别出满足多目标约束的候选材料。值得注意的是，我们通过真实合成实验验证了PeroMAS在物理世界中的有效性。