将领域专用语言模型与AI测量工具整合用于确定性原子分辨率实验
Integrating Domain-Specialized Language Models with AI Measurement Tools for Deterministic Atomic-Resolution Experimentation
AI总结：
本文提出一种框架，通过专门的小语言模型实现扫描探针显微镜的自主控制，结合AI驱动的测量工具，实现室温下的原子分辨率SPM实验，提升实验可靠性和可控性。
摘要：
基于大语言模型的自动驾驶实验室有望通过通用实验自动化变革科学发现。然而，在高精度平台上实现这一愿景仍具挑战性，需确定性执行和在严格物理约束下有效的领域适应。我们通过一个专注于小语言模型的框架来解决这些需求，用于扫描探针显微镜的自主控制，协调任务专用模型与AI驱动的测量工具。我们展示了在室温下实时、原子分辨率的SPM实验，实现了指令级控制和多步骤实验规划。微调使困惑度从1.44降至1.20，并提高了可靠性，适应后的模型达到99.3%和95.2%的指令准确性，优于OpenAI o4-mini在领域特定任务上的表现。该架构在保持确定性执行的同时降低了计算成本，使部署在消费级硬件上成为可能。本文通过模块化、领域专用的架构将概率语言模型与确定性实验控制相结合，提供了一条通往跨多样科学平台可扩展和可信的自动驾驶实验室的通用路径。
英文摘要：
Self-driving laboratories based on large language models promise to transform scientific discovery through general experimental automation. However, realizing this vision on precision platforms remains challenging, requiring deterministic execution and effective domain adaptation under strict physical constraints. We address these requirements through a framework that specializes in small language models for autonomous control of scanning probe microscopy, coordinating task-specific models with AI-driven measurement tools. We demonstrate real-time, atomic-resolution SPM experiments at room temperature, achieving instruction-level control and multi-step experimental planning. Fine-tuning reduces perplexity from 1.44 to 1.20 and improves reliability, with the adapted model reaching 99.3% and 95.2% command accuracy, outperforming OpenAI o4-mini on domain-specific tasks. This architecture achieves lower computational cost while maintaining deterministic execution and enabling deployment on consumer-grade hardware. This work bridges probabilistic language models with deterministic experimental control through a modular, domain-specialized architecture, providing a generalizable pathway toward scalable and trustworthy self-driving laboratories across diverse scientific platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
