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ABSTRACT Carbon nanotube (CNT) research is strongly constrained by the coupled relationships among synthesis conditions, multiscale structure, and functional performance, which make rational optimization difficult using trial‐and‐error alone. Artificial intelligence (AI) is emerging as a useful set of tools for navigating this complexity, particularly under data‐scarce experimental conditions. This review summarizes recent progress in AI‐assisted CNT research across three connected stages: synthesis optimization, automated characterization, and application‐oriented structure–property modeling. Current evidence shows that supervised learning and Bayesian optimization can accelerate the exploration of CNT growth windows, whereas computer vision and spectral learning can convert microscopy and spectroscopy outputs into quantitative descriptors for downstream modeling. We further discuss emerging language model‐based literature mining workflows while emphasizing that their CNT‐specific validation remains less mature than that of synthesis and characterization models. Finally, we outline the major barriers to model transferability, including small heterogeneous datasets, inconsistent reporting, and uncertainty in characterization‐derived labels and highlight how standardized descriptors and closed‐loop workflows could make AI more actionable in CNT research.
Zhao et al. (Wed,) studied this question.