用多智能体协作实现可解释的材料自动发现。
PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration
- 基于语言推理的多智能体系统生成科学假说并验证
- 纳米螺旋案例中探索效率与性能均提升
- 适合需要透明决策过程的材料研发团队
复杂的化学空间与人类知识局限性带来的偏见，给材料发现带来巨大挑战。现有智能方法过度依赖数值计算，导致探索效率低且结果难以解释。为此，我们提出一个由语言推理多智能体系统（MAS）驱动的原则引导材料发现框架——PriM。该框架在多智能体圆桌讨论机制下，实现假设自动生成与实验验证的闭环，兼顾系统性探索与科学严谨性。以纳米螺旋为例，该方法显著提升了材料探索速率与性能表现，并提供可追溯的推理路径。本研究建立了一种自动化且透明的材料发现范式，对功能材料的理性设计具有广泛意义。代码已开源至我们的 exttt{GitHub}。
原文摘要 · Abstract (English)
Complex chemical space and limited knowledge scope with biases holds immense challenge for human scientists, yet in automated materials discovery. Existing intelligent methods relies more on numerical computation, leading to inefficient exploration and results with hard-interpretability. To bridge this gap, we introduce a principles-guided material discovery system powered by language inferential multi-agent system (MAS), namely PriM. Our framework integrates automated hypothesis generation with experimental validation in a roundtable system of MAS, enabling systematic exploration while maintaining scientific rigor. Based on our framework, the case study of nano helix demonstrates higher materials exploration rate and property value while providing transparent reasoning pathways. This approach develops an automated-and-transparent paradigm for material discovery, with broad implications for rational design of functional materials. Code is publicly available at our \href{https://github.com/amair-lab/PriM}{GitHub}.
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