Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research

Tokyo 152 - 8552, Japan E - mail: kan.hatakeyama [ [ at ] ] weblab.t.u - tokyo.ac.jp Abstract This review explores the potential of foundation models to advanc e 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 fea sibility 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. Th is 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. Keywords Laboratory Automation; Foundation Models; Robotics; Artificial Intelligence; Materials Science 1. Expectations for Foundation Models in Materials Laboratory Automation Laboratory automation, a technology aimed at automating experimental research, is expected to pave the way for a new research paradigm in materials science [1, 2, 3] . By rapidly and comprehensively executing numerous experiments, laboratory automation accelerates research, enhances reproducibility through precisely controlled robotic processes, and enables swift and distributed knowledge sharing among researchers worldwide [1] . This technology is anticipated to contribute significantly to the development of crucial devices and compounds, including catalyst s for energy and chemical conversions, environmentally friendly plastics, solar cells, secondary batteries, fuel cells, thermoelectric conversion modules, nuclear fusion reactors, quantum computers, and energy - efficient computing systems [1, 4, 5] . The success of next - generation laboratory automation depends not only o n experimental hardware but also o n the utilization of artificial intelligence (AI), especially foundation models. Foundation models represent a new AI paradigm encompassing large language models like GPT - 4 [6], multimodal models, and agent - related technologies. These foundation models and generative AI have begun to influenc e chemistry and materials science [7], giving rise to diverse applications including molecular and materials design [8, 9, 10], reaction pathway exploration [11], catalyst design [12], and even autonomous planning of chemical experiments [13] . Additionally, foundation models are being expanded to hardware control mechanisms, enabling natural language - driven robotic operations [14, 15] .