Operating Robotic Laboratories with Large Language Models and Teachable Agents

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.