PeroMAS: A Multi-agent System of Perovskite Material Discovery

TL;DR

A multi-agent system for perovskite material discovery, named PeroMAS, which successfully identified candidate materials satisfying multi-objective constraints and is verified in the physical world through real synthesis experiments.

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 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.

Figures (8)

• Figure 1: (a) Despite ML methods focus on single-objective mapping, they create high-precision specialized models that populate the PSC tool pool; (b) LLMs integrate domain knowledge for multi-objective tasks but may suffer from uncontrolled constraints and lack invocation for PSC tools; (c) We propose PeroMAS to orchestrate functional agents, which dynamically retrieving and utilizing tools from the PSC tool pool and achieving high-fidelity solutions.
• Figure 2: A task-oriented overview of related work on AI for PSCs, including knowledge preparation, design, dry-lab, and wet-lab. PeroMAS automatically executes the full process for perovskite research and validates the candidates designed by PeroMAS through wet-lab experiments.
• Figure 3: The system is centrally orchestrated by a Meta Agent responsible for global planning and memory management. It directs four functional agents, which are equipped with specialized domain tools via MCP to execute the closed-loop discovery workflow—spanning knowledge preparation, inverse design, property prediction, and mechanistic analysis.
• Figure 4: Overview of PeroMAS-Bench dataset.(a) Hierarchical task distribution across three complexity levels. (b) Capability contribution flow mapping agents to qualitative dimensions, where thickness indicates challenge intensity.
• Figure 5: Practical validation of the PeroMAS system.(a) Used wet-lab devices for perovskite material synthesis. (b) Experimental results displaying the fabricated perovskite devices and their corresponding J-V curves for PCE evaluation.