用通用模型让实验室自动完成材料实验规划与操作
Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research
- 用大模型和多模态系统实现实验规划与硬件控制
- 可处理复杂动态任务，但精度与安全仍需提升
- 适合材料研究与自动化交叉领域科研人员
本文综述了基础模型在材料与化学科学实验室自动化中的潜力。强调其双重作用：认知功能（实验规划与数据分析）和物理功能（硬件操作）。传统自动化依赖专用、僵化的系统，而基础模型凭借通用智能与多模态能力提供更强适应性。近期进展表明，大型语言模型（LLMs）与多模态机器人系统已能应对复杂动态实验任务。然而，硬件精确操控、多模态数据融合及运行安全仍是重大挑战。本文提出发展路线图，倡导跨学科协作、基准建立与人机协同策略，以推动全自主实验实验室的实现。
原文摘要 · Abstract (English)
This review explores the potential of foundation models to advance laboratory automation in the materials and chemical sciences. It emphasizes the dual roles of these models: cognitive functions for experimental planning and data analysis, and physical functions for hardware operations. While traditional laboratory automation has relied heavily on specialized, rigid systems, foundation models offer adaptability through their general-purpose intelligence and multimodal capabilities. Recent advancements have demonstrated the feasibility of using large language models (LLMs) and multimodal robotic systems to handle complex and dynamic laboratory tasks. However, significant challenges remain, including precision manipulation of hardware, integration of multimodal data, and ensuring operational safety. This paper outlines a roadmap highlighting future directions, advocating for close interdisciplinary collaboration, benchmark establishment, and strategic human-AI integration to realize fully autonomous experimental laboratories.
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