Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research

The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have pro-vided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by: (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous systems, and reducing reliance on conventional trial-and-error approaches; (2) im-proving the accuracy and efficiency of CNT characterizations; and (3) accelerating the develop-ment of CNT applications across several fields such as electronics, composites, and biomedical fields. The review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward.