AutoLabs让AI自主完成化学实验，靠自我修正和多智能体协作提升可靠性。
AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation
- 用多智能体分解任务，结合工具计算化学配比。
- 自校正机制使复杂合成错误率降低85%以上。
- 适合需要高精度自动化的实验室科研人员。
通过自驱动实验室（SDLs）自动化化学研究有望加速科学发现，但底层AI代理的可靠性与精细性能仍是关键且被忽视的挑战。本文提出AutoLabs，一种具备自我修正能力的多智能体架构，可将自然语言指令自动转化为适用于高通量液体处理器的可执行协议。系统通过对话交互、任务分解、工具辅助的化学计量计算，并在生成硬件文件前迭代自我修正。我们设计了五项逐步复杂的基准实验，涵盖从简单样品制备到多板定时合成。通过对20种代理配置的系统性消融研究，评估了推理能力、架构设计（单/多智能体）、工具使用与自我修正机制的影响。结果表明，代理推理能力是成功最关键因素，在复杂任务中使化学量测误差（nRMSE）降低超85%。结合多智能体架构与迭代自校正，AutoLabs在复杂多步合成中达到近专家级程序准确率（F1-score > 0.89）。这些发现为构建可靠可信的自主实验室AI伙伴提供了清晰蓝图，凸显模块化设计、高级推理与自校正的协同效应，确保高风险科学应用中的性能与可靠性。代码：https://github.com/pnnl/autolabs
原文摘要 · Abstract (English)
The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput liquid handler. The system engages users in dialogue, decomposes experimental goals into discrete tasks for specialized agents, performs tool-assisted stoichiometric calculations, and iteratively self-corrects its output before generating a hardware-ready file. We present a comprehensive evaluation framework featuring five benchmark experiments of increasing complexity, from simple sample preparation to multi-plate timed syntheses. Through a systematic ablation study of 20 agent configurations, we assess the impact of reasoning capacity, architectural design (single- vs. multi-agent), tool use, and self-correction mechanisms. Our results demonstrate that agent reasoning capacity is the most critical factor for success, reducing quantitative errors in chemical amounts (nRMSE) by over 85% in complex tasks. When combined with a multi-agent architecture and iterative self-correction, AutoLabs achieves near-expert procedural accuracy (F1-score > 0.89) on challenging multi-step syntheses. These findings establish a clear blueprint for developing robust and trustworthy AI partners for autonomous laboratories, highlighting the synergistic effects of modular design, advanced reasoning, and self-correction to ensure both performance and reliability in high-stakes scientific applications. Code: https://github.com/pnnl/autolabs
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。