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Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models

arXiv AI Archived Apr 07, 2026 ✓ Full text saved

arXiv:2604.03286v1 Announce Type: new Abstract: The control of complex laboratory instrumentation often requires significant programming expertise, creating a barrier for researchers lacking computational skills. This work explores the potential of large language models (LLMs), such as ChatGPT, and LLM-based artificial intelligence (AI) agents to enable efficient programming and automation of scientific equipment. Through a case study involving the implementation of a setup that can be used as a

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    Computer Science > Artificial Intelligence [Submitted on 25 Mar 2026] Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models Yong Xie, Kexin He, Andres Castellanos-Gomez The control of complex laboratory instrumentation often requires significant programming expertise, creating a barrier for researchers lacking computational skills. This work explores the potential of large language models (LLMs), such as ChatGPT, and LLM-based artificial intelligence (AI) agents to enable efficient programming and automation of scientific equipment. Through a case study involving the implementation of a setup that can be used as a single-pixel camera or a scanning photocurrent microscope, we demonstrate how ChatGPT can facilitate the creation of custom scripts for instrumentation control, significantly reducing the technical barrier for experimental customization. Building on this capability, we further illustrate how LLM-assisted tools can be extended into autonomous AI agents capable of independently operating laboratory instruments and iteratively refining control strategies. This approach underscores the transformative role of LLM-based tools and AI agents in democratizing laboratory automation and accelerating scientific progress. Comments: 16 pages, 5 figures. Accepted manuscript published in Small Structures. Supporting data and code available at this https URL Subjects: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci); Human-Computer Interaction (cs.HC) Cite as: arXiv:2604.03286 [cs.AI]   (or arXiv:2604.03286v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.03286 Focus to learn more Journal reference: Small Structures, 2025, 6(8), 2500173 Related DOI: https://doi.org/10.1002/sstr.202500173 Focus to learn more Submission history From: Yong Xie [view email] [v1] Wed, 25 Mar 2026 22:01:45 UTC (988 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cond-mat cond-mat.mtrl-sci cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
    Category
    ◬ AI & Machine Learning
    Published
    Apr 07, 2026
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    Apr 07, 2026
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