Carbon nanotube (CNT) has promising applications in several fields due to their excellent thermal, electrical, mechanical, and biocompatible properties. However, the complexity of its structure leads to the problems of computationally intensive and inefficient synthetic characterization optimization and prediction by traditional research methods, which seriously restricts the development process. Machine learning (ML), as an emerging technology, has been widely used in CNT research due to its ability to reduce computational cost, shorten the development cycle, and improve the accuracy. ML not only optimizes the synthetic control parameters for precise structural control, but also combines various imaging and spectroscopic techniques to significantly improve the accuracy and efficiency of characterization. In addition, ML helps to improve the performance of CNT devices at the optimization and prediction levels, and achieve accurate performance prediction. However, ML in CNT research still faces challenges such as algorithmic processing of complex data situations, insufficient space for algorithmic combined optimization, and lack of model interpretability. Future research can focus on developing more efficient ML algorithms and unified standardized databases, exploring the deep integration of different algorithms, further improving the performance of ML in CNT research, and promoting its application in more fields.

Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube
    Abstract
    1 Introduction
    2 Machine learning methods
        2.1 Supervised learning
        2.2 Reinforcement learning
        2.3 Deep learning
    3 Synthesis process control of ML-driven CNT
        3.1 Synthesis parameter control of ML-driven CNT
        3.2 Structural control of ML-driven CNT
    4 ML-driven CNT characterization
        4.1 ML characterization of CNT structures
        4.2 ML characterization of CNT components
    5 ML-driven CNT optimization and prediction
        5.1 ML-driven CNT optimization
        5.2 ML-driven CNT performance prediction
    6 Summary and outlook
        6.1 Summary
        6.2 Challenges
        6.3 Outlook
    Acknowledgements 
    References

• Qiutong Li(Shandong Engineering Laboratory for Preparation and Application of High‑Performance Carbon‑Materials, College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong 266061, China)
• Chenyu Gao(Shandong Engineering Laboratory for Preparation and Application of High‑Performance Carbon‑Materials, College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong 266061, China)
• Xijun Zhang(Shandong Engineering Laboratory for Preparation and Application of High‑Performance Carbon‑Materials, College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong 266061, China)
• Xinyue Zhao(Shandong Engineering Laboratory for Preparation and Application of High‑Performance Carbon‑Materials, College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong 266061, China)
• Yan He(Shandong Engineering Laboratory for Preparation and Application of High‑Performance Carbon‑Materials, College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong 266061, China, Shandong Provincial Key Laboratory of Advanced Energy Storage Technology, Qingdao University, Qingdao 266071, Shandong, China)
• Dianming Chu(Shandong Engineering Laboratory for Preparation and Application of High‑Performance Carbon‑Materials, College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong 266061, China, Shandong Province Key Laboratory of Rubber-Based High-Performance Composites and Advanced Manufacturing, Qingdao, Shandong, China)
• Wenjuan Bai(Shandong Engineering Laboratory for Preparation and Application of High‑Performance Carbon‑Materials, College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong 266061, China, Shandong Province Key Laboratory of Rubber-Based High-Performance Composites and Advanced Manufacturing, Qingdao, Shandong, China)
• Qi Jin(Shandong Province Key Laboratory of Rubber-Based High-Performance Composites and Advanced Manufacturing, Qingdao, Shandong, China, Tongli Tire Co., Ltd., Huaqin Industrial Park, Yanzhou District, Jining City 272106, Shandong, China)