OPERATING ROBOTIC LABORATORIES WITH LARGE LANGUAGE MODELS AND TEACHABLE AGENTS
Submission Track: Paper Track (Tiny Paper)
Submission Category: All of the above
Keywords: LLMs, agentic AI, Autonomous experiments, Self-driving laboratories
Supplementary Material: zip
TL;DR: We introduce a pipeline using agentic AI powered by LLMs to automate and simplify complex, multi-task workflows in a robotic materials design station, making advanced scientific facilities more intelligent and user-friendly.
Abstract: Advanced scientific user facilities, including self-driving laboratories, are revolutionizing
scientific discovery by automating repetitive tasks and enabling rapid
experimentation. However, these facilities must continuously evolve to support
new experimental workflows, adapt to diverse user projects, and meet growing demands
for evermore sophisticated instrumentation. This continuous development
introduces significant operational complexity, necessitating a focus on usability,
reproducibility, and intuitive human-instrument interaction. In this work, we explore
the integration of agentic AI, powered by Large Language Models (LLMs),
as a transformative tool to achieve this goal. We present our approach to developing
a pipeline for operating a robotic station dedicated to the design of novel
materials. Specifically, we evaluate the potential of various LLMs as trainable
scientific assistants for orchestrating complex, multi-task workflows, optimizing
their performance through human input and iterative learning. We demonstrate
the ability of AI agents to bridge the gap between advanced automation and userfriendly
operation, paving the way for more adaptable and intelligent scientific
facilities.
AI4Mat Journal Track: Yes
Submission Number: 49
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