Title: Predictive modelling of carbon nanotube structures using machine learning techniques

Authors: Pawan Whig; Imran Ahmed Khan; Amrita Rai; Owais Ahmad Shah; M. Nasim Faruque; Jaishanker Prasad Keshari; Mudit Wadhwa

Addresses: Vivekananda Institute of Professional Studies-TC, New Delhi, India ' Electronics and Communication Engineering, Jamia MIllia Islamia, New Delhi-110025, India ' Department of Electronics and Communication Engineering, LLOYD Institute of Engineering and Technology, Knowledge Park II, Greater Noida-201306, India ' School of Engineering and Technology, K.R. Mangalam University, Gurugram (Haryana), India ' Jamia MIllia Islamia, New Delhi-110025, India ' Government Engineering College Sheikhpura, Bazidpur, Bihar 811105, India ' Jamia MIllia Islamia, New Delhi-110025, India

Abstract: This paper explores predictive modelling techniques applied to the structural analysis of carbon nanotubes (CNTs) using a dataset encompassing 10,721 initial and calculated atomic coordinates, alongside their intricate chiral networks. Derived from simulation software, BIOVIA materials studio CASTEP, this dataset serves as the foundation for employing advanced machine learning methodologies. Our research aims to decode the nuanced complexities inherent in CNT structures. By leveraging cutting-edge machine learning approaches, we seek to revolutionise the understanding and predictive capabilities regarding CNT architectures. The significance of our findings resonates deeply within materials science and nanotechnology, promising to streamline the comprehension and utilisation of CNTs across diverse domains, spanning electronics to materials engineering. This study marks a pivotal stride towards automating and expediting the development of nanomaterials, fostering innovation in a field where precision and efficiency are paramount. Our work showcases the potential for transformative advancements in harnessing CNTs for practical applications, propelling the integration of these nanostructures into real-world technologies.

Keywords: carbon nanotubes; CNTs; predictive modelling; machine learning; structural analysis; materials studio CASTEP; nanomaterials; atomic coordinates; chiral networks; nanotechnology; materials science; simulation data.

DOI: 10.1504/IJCVR.2026.150342

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.1, pp.67 - 80

Received: 22 Nov 2023
Accepted: 25 Dec 2023
Published online: 10 Dec 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article