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[2602.13312] PeroMAS: A Multi-agent System of Perovskite Material Discovery
Summary
PeroMAS introduces a multi-agent system for discovering perovskite materials, enhancing efficiency in photovoltaic research through a comprehensive workflow integration.
Why It Matters
This research addresses the limitations of existing AI models in perovskite material discovery by proposing a holistic approach that integrates various processes. As perovskite solar cells are pivotal for renewable energy, advancements in their development can significantly impact energy efficiency and sustainability.
Key Takeaways
- PeroMAS enhances the discovery of perovskite materials by integrating multiple workflows.
- The system utilizes Model Context Protocols (MCPs) for effective material design.
- Real-world synthesis experiments validate PeroMAS's effectiveness in identifying candidate materials.
- The multi-agent approach outperforms traditional AI models in efficiency.
- This research contributes to the advancement of photovoltaic technology, crucial for sustainable energy.
Computer Science > Multiagent Systems arXiv:2602.13312 (cs) [Submitted on 10 Feb 2026] Title:PeroMAS: A Multi-agent System of Perovskite Material Discovery Authors:Yishu Wang, Wei Liu, Yifan Li, Shengxiang Xu, Xujie Yuan, Ran Li, Yuyu Luo, Jia Zhu, Shimin Di, Min-Ling Zhang, Guixiang Li View a PDF of the paper titled PeroMAS: A Multi-agent System of Perovskite Material Discovery, by Yishu Wang and 10 other authors View PDF HTML (experimental) Abstract: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 anal...