用于纳米尺度表征科学仪器操作的智能体AI
Agentic AI for operating scientific instruments for nanoscale characterization
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中文总结 AI 辅助
本文提出一种基于模型上下文协议（MCP）的智能体AI框架，通过3个AFM智能体实现原子力显微镜（AFM）的自动化操作，可安全执行命令、调优参数与后处理，其性能与专家操作相当。
中文摘要 AI 辅助
操作原子力显微镜（AFM）等科学仪器需要专家进行连续决策：训练有素的用户定义实验意图，将其转化为仪器命令，评估传入数据，调整成像参数，并对最终图像进行后处理。现有自动化技术通常仅通过硬编码例程、任务特定控制器或训练好的机器学习模型来处理此工作流程的部分环节。本文提出一种智能体AI框架，该框架使用通过模型上下文协议（MCP）连接到仪器功能的通用工具增强型大语言模型，来执行AFM工作流程的可操作部分。该框架包含3个基于MCP的智能体：AFM Messenger将自然语言指令转换为经校验的仪器命令；AFM Pilot通过大语言模型（LLM）评估图像质量，必要时调整成像参数；AFM Doctor诊断图像伪影，并从预批准的工具集中应用透明后处理。由于语言模型执行图像评估而非固定标量目标或外部优化器，该策略可跨样本类型和成像模式应用，无需特定重新训练。执行前的歧义校验层确保硬件安全操作。与微调及现成工具使用模型的基准测试显示，这种受保护的执行层（而非仅模型能力）将错误命令执行率降至零。在不同样本的活体实验中，AFM Pilot在图像质量、迭代次数和调优时间上与专家操作人员表现相当，无显著差异。这些结果展示了科学仪器智能体操作的安全路径，实验意图仍由人类定义，而命令执行、基于图像的调优及后处理则委托给AI智能体。
英文摘要
Operating a scientific instrument such as an atomic force microscope (AFM) requires continuous expert decision-making. A trained user defines the experimental intent, translates it into instrument commands, assesses incoming data, adjusts imaging parameters, and post-processes the final image. Existing automation usually addresses only parts of this workflow through hard-coded routines, task-specific controllers, or trained machine-learning models. Here we present an agentic-AI framework that operates the executable part of the AFM workflow using a general-purpose, tool-augmented large language model connected to instrument functions through the Model Context Protocol (MCP). The framework consists of 3 MCP-based agents: AFM Messenger converts natural-language instructions into checked instrument commands; AFM Pilot assesses image quality through a large language model (LLM) and, if necessary, adapts imaging parameters; and AFM Doctor diagnoses image artifacts and applies transparent post-processing from a pre-approved tool set. Because the language model performs image assessment rather than a fixed scalar objective or external optimizer, the same strategy can be applied across sample types and imaging modes without specific retraining. Safe hardware operation is enforced through an ambiguity check layer before execution. Benchmarking against fine-tuned and off-the-shelf tool-using models shows that this guarded execution layer, rather than model capability alone, reduces wrong-command execution to zero. In live experiments on different samples, AFM Pilot matched expert operators in image quality, iteration count, and tuning time, with no significant difference. These results demonstrate a safe route to agentic operation of scientific instruments, where experimental intent remains human-defined while command execution, image-based tuning, and post-processing are delegated to AI agents.
