Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research

Understand

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.