Emergence of Artificial Intelligence in the Structure-Controllable Synthesis of Carbon Nanotubes

Carbon nanotubes (CNTs) exhibit remarkable properties that have spurred extensive exploration across domains such as integrated circuits, aerospace, and energy storage. With application scenarios becoming increasingly specialized, the structure-controllable synthesis of CNTs faces escalating challenges. This review summarizes chemical vapor deposition (CVD) techniques for controlled CNT synthesis and examines the structural-control mechanisms during growth, emphasizing the critical factors influencing CNT diameter, electronic properties, and chirality. Conventional trial-and-error approaches have become inadequate in addressing the demands for precise structural manipulation during synthesis and complex variable optimization in scaled production. Recently, artificial intelligence (AI) has substantially advanced scientific research and technological innovation. In the concluding perspective, we highlight emerging paradigms that incorporate AI into CNT synthesis, where the synergy between data-driven experimentation and physics-informed constraints may enable the development of accurate and efficient digital twins of CNT growth systems. Such approaches offer promise for the inverse design of synthesis routes and deeper insight into structure-control mechanisms. We conclude by identifying promising directions for AI-enhanced CNT synthesis, including multiscale computational simulations, catalyst design, automated experimental platforms, and pilot-scale production, which may collectively advance the Frontier of precision nanomanufacturing.