用自然语言让科学家一键生成实验室自动化流程
From Prompts to Protocols: An AI Agent for Laboratory Automation
- 结合大模型与实验编排系统，用对话方式设计实验流程
- 在三类实验室模拟中实现97%首试成功率，操作量减少十倍
- 适合科研人员快速搭建自动化实验，无需编写代码
自动化科学实验室可加速新材料、药物等的发现与测试，提升速度、安全性和可重复性。但构建和运行自主实验室需协调多种仪器与机器人，迫使科学家编写代码、管理配置文件并处理复杂的软件架构。我们提出一种AI代理架构，将大语言模型与实验编排系统（EOS）融合，使科学家可通过自然语言交互式创建和监控自动化实验流程。该代理在智能循环中运行，具备自动验证与纠错能力，支持完整的实验生命周期：协议创建、执行与监控，以及闭环优化实验。配备可视化图编辑器，以节点图形式呈现协议，与AI代理的表示同步，实现AI辅助与手动构建的无缝切换。在涵盖化学、生物和材料科学的三个模拟自动化实验室中评估，该系统实现97%的首次协议生成成功率，并使所需操作动作减少一个数量级。
原文摘要 · Abstract (English)
Automating science laboratories enables faster, safer, more accurate, and more reproducible execution of protocols, accelerating the discovery and testing of new materials, drugs, and more. However, setting up and running autonomous labs requires coordinating numerous instruments and robots, forcing scientists to write code, manage configuration files, and navigate complex software infrastructure. We present an AI agent architecture that integrates large language models with laboratory orchestration, enabling scientists to interactively create and monitor automated lab protocols using natural language. Integrated into the Experiment Orchestration System (EOS), the AI agent operates under an agentic loop with automated validation and error correction, and supports the complete experimental lifecycle: creating protocols, running and monitoring both protocols and closed-loop optimization campaigns, and analyzing results. A visual graph editor renders protocols as interactive node-based diagrams synchronized with the AI agent's protocol representation, enabling seamless alternation between AI-assisted and manual protocol construction. Evaluated on three simulated automated labs spanning chemistry, biology, and materials science, the AI agent achieves a 97% first-attempt protocol generation success rate and an order of magnitude reduction in required interface actions.
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