PeroMAS: A Multi-agent System of Perovskite Material Discovery

TL;DR

PeroMAS is a multi-agent system that integrates various tools and constraints to automate and optimize the discovery of new perovskite materials, improving efficiency and success rate over traditional methods.

Contribution

This paper introduces PeroMAS, a novel multi-agent framework that enables end-to-end perovskite material discovery with physical constraints, surpassing existing AI approaches.

Findings

Significantly improves discovery efficiency over single LLM or search strategies.

Successfully identifies candidate materials satisfying multi-objective constraints.

Verifies effectiveness 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.