Research Trend Analysis in the Field of Self-Driving Labs Using Network Analysis and Topic Modeling

A self-driving lab (SDL) system that automates experimental design, data collection, and analysis using robotics and artificial intelligence (AI) technologies. Its significance has grown substantially in recent years. This study analyzes the overall SDL research trends, examines changes in specific topics, visualizes the relational structure between authors to identify key contributors, and extracts major themes from extensive texts to highlight essential research content. To achieve these objectives, trend analysis, network analysis, and topic modeling were conducted on 352 research papers collected from the Web of Science between 2004 and 2023. To ensure the validity of the topic modeling results, a topic correlation matrix was also performed. The results revealed three key findings. First, SDL research has surged since 2019, driven by advancements in AI technologies, reflecting heightened activity in this field. Second, modern scientific research is advancing with a focus on data-driven approaches, artificial intelligence applications, and experimental optimization through the utilization of SDLs. Third, SDL research exhibits interdisciplinary convergence, encompassing material optimization, biological processes, and AI predictive algorithms. This study underscores the growing importance of SDLs as a research tool across diverse academic disciplines and provides practical insights into sustainable future scientific research directions and strategic approaches.