让材料发现机器人自主规划，融合物理规律与科学家经验。
Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
- 用大模型生成多步发现流程，自动规划研究路径。
- 代码生成器调用物理模型，提升结果稳定性与创新性。
- 支持科学家反馈与错误修复，适合复杂材料研发场景。
我们致力于设计具备更高自主性的语言代理，用于晶体材料发现。现有研究多限制代理在预设流程中完成特定任务，而本文旨在根据高层次目标与科学家直觉自动规划工作流。为此，提出MAPPS框架，整合规划、物理与科学家协作：工作流规划器利用大语言模型生成结构化多步流程；工具代码生成器合成执行代码，包括调用编码物理规律的力场基础模型；科学中介者负责协调沟通、接收科学家反馈，并通过错误反思与恢复保障鲁棒性。实验表明，相较于先前生成模型，MAPPS在MP-20数据集上实现稳定性、唯一性和新颖性率五倍提升。跨多种任务的大量实验验证了其作为自主材料发现框架的潜力。
原文摘要 · Abstract (English)
We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within predefined workflows, we aim to automate workflow planning given high-level goals and scientist intuition. To this end, we propose Materials Agent unifying Planning, Physics, and Scientists, known as MAPPS. MAPPS consists of a Workflow Planner, a Tool Code Generator, and a Scientific Mediator. The Workflow Planner uses large language models (LLMs) to generate structured and multi-step workflows. The Tool Code Generator synthesizes executable Python code for various tasks, including invoking a force field foundation model that encodes physics. The Scientific Mediator coordinates communications, facilitates scientist feedback, and ensures robustness through error reflection and recovery. By unifying planning, physics, and scientists, MAPPS enables flexible and reliable materials discovery with greater autonomy, achieving a five-fold improvement in stability, uniqueness, and novelty rates compared with prior generative models when evaluated on the MP-20 data. We provide extensive experiments across diverse tasks to show that MAPPS is a promising framework for autonomous materials discovery.
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