Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube

Qiutong Li; Qi Jin; Chenyu Gao; Zhang Xijun; Xinyue Zhao; Yan He; Dianming Chu; Wenjuan Bai

doi:10.1007/s42823-025-00952-0

Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube

Carbon Letters
Abbr : Carbon Lett.
2025, 35(5), pp.1893~1931
DOI : 10.1007/s42823-025-00952-0
Publisher : Korean Carbon Society
Research Area : Natural Science > Natural Science General > Other Natural Sciences General
Received : May 13, 2025
Accepted : July 10, 2025
Published : December 11, 2025

Qiutong Li ¹, Qi Jin ², Chenyu Gao ¹, Zhang Xijun ¹, Xinyue Zhao ¹, Yan He ¹, Dianming Chu ¹, Wenjuan Bai ¹

¹Qingdao University of Science and Technology
²Shandong Province Key Laboratory of Rubber-Based High-Performance Composites and Advanced Manufacturing

Accredited

ABSTRACT

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.

KEYWORDS

Carbon nanotube, Machine learning, Optimization, Characterization, Prediction

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