@article{ART003273788},
author={Li Qiutong and Jin Qi and Gao Chenyu and Zhang Xijun and Zhao Xinyue and He Yan and Chu Dianming and Bai Wenjuan},
title={Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube},
journal={Carbon Letters},
issn={1976-4251},
year={2025},
volume={35},
number={5},
pages={1893-1931},
doi={10.1007/s42823-025-00952-0}
TY - JOUR
AU - Li Qiutong
AU - Jin Qi
AU - Gao Chenyu
AU - Zhang Xijun
AU - Zhao Xinyue
AU - He Yan
AU - Chu Dianming
AU - Bai Wenjuan
TI - Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube
JO - Carbon Letters
PY - 2025
VL - 35
IS - 5
PB - Korean Carbon Society
SP - 1893
EP - 1931
SN - 1976-4251
AB - 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.
KW - Carbon nanotube;Machine learning;Optimization;Characterization;Prediction
DO - 10.1007/s42823-025-00952-0
ER -
Li Qiutong, Jin Qi, Gao Chenyu, Zhang Xijun, Zhao Xinyue, He Yan, Chu Dianming and Bai Wenjuan. (2025). Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube. Carbon Letters, 35(5), 1893-1931.
Li Qiutong, Jin Qi, Gao Chenyu, Zhang Xijun, Zhao Xinyue, He Yan, Chu Dianming and Bai Wenjuan. 2025, "Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube", Carbon Letters, vol.35, no.5 pp.1893-1931. Available from: doi:10.1007/s42823-025-00952-0
Li Qiutong, Jin Qi, Gao Chenyu, Zhang Xijun, Zhao Xinyue, He Yan, Chu Dianming, Bai Wenjuan "Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube" Carbon Letters 35.5 pp.1893-1931 (2025) : 1893.
Li Qiutong, Jin Qi, Gao Chenyu, Zhang Xijun, Zhao Xinyue, He Yan, Chu Dianming, Bai Wenjuan. Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube. 2025; 35(5), 1893-1931. Available from: doi:10.1007/s42823-025-00952-0
Li Qiutong, Jin Qi, Gao Chenyu, Zhang Xijun, Zhao Xinyue, He Yan, Chu Dianming and Bai Wenjuan. "Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube" Carbon Letters 35, no.5 (2025) : 1893-1931.doi: 10.1007/s42823-025-00952-0
Li Qiutong; Jin Qi; Gao Chenyu; Zhang Xijun; Zhao Xinyue; He Yan; Chu Dianming; Bai Wenjuan. Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube. Carbon Letters, 35(5), 1893-1931. doi: 10.1007/s42823-025-00952-0
Li Qiutong; Jin Qi; Gao Chenyu; Zhang Xijun; Zhao Xinyue; He Yan; Chu Dianming; Bai Wenjuan. Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube. Carbon Letters. 2025; 35(5) 1893-1931. doi: 10.1007/s42823-025-00952-0
Li Qiutong, Jin Qi, Gao Chenyu, Zhang Xijun, Zhao Xinyue, He Yan, Chu Dianming, Bai Wenjuan. Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube. 2025; 35(5), 1893-1931. Available from: doi:10.1007/s42823-025-00952-0
Li Qiutong, Jin Qi, Gao Chenyu, Zhang Xijun, Zhao Xinyue, He Yan, Chu Dianming and Bai Wenjuan. "Advances in data-driven integrated design synthesis optimization and prediction of carbon nanotube" Carbon Letters 35, no.5 (2025) : 1893-1931.doi: 10.1007/s42823-025-00952-0
