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Context <p>The accurate visualization and modeling of nanostructures, such as carbon nanotubes (CNTs) and graphene play a crucial role in advancing nanoscale research and applications. The versatile properties of graphene and CNTs make them fashionable among the scientific community. Modeling and simulation play an important role in understanding and predicting the behavior of these nanomaterials. Traditional modeling approaches often rely on specialized software and complex computational methods. However, the advent of versatile programming languages like Python has opened new avenues for simulating and visualizing nanostructures. Python’s extensive libraries and user-friendly syntax make it an attractive tool for researchers aiming to model CNTs and graphene structures. The study demonstrated 3D visualization of graphene and two configurations of CNTs, namely single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) marvelously using Python-based libraries. The Python codes of 3D nanostructure models advance the nanoscale research.</p> Method <p>The present study investigates the potential and limitations of Python-based libraries with a primary focus on Mayavi and PyVista for rendering and analyzing these nanostructures, while Mayavi offers a high-level 3D visualization interface built on visualization toolkit (VTK). The attempts to generate structurally accurate CNTs and graphene lattices revealed several critical challenges, including improper bonding representation, lattice distortions, and scaling inconsistencies. Moreover, the study presents the implementation strategies, code structure, and visual output limitations encountered during the modeling process. To provide a comparative landscape, additional tools such as Matplotlib (for 2D visualization), VPython (for quick atomic modeling), and atomic simulation environment (ASE) are briefly evaluated. The study also describes the role of NumPy, SciPy, and Pymatgen in computational geometry, structural logic, and periodic boundary condition (PBC) modeling.</p> Graphical Abstract <p></p>

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3D visualization of graphene and carbon nanotubes using Python: a study

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Ashutosh Sharma,
  • Sanjeev Kumar,
  • Kuldeep Kumar,
  • P. Arun,
  • Manveer Singh,
  • Deepti Jain

摘要

Context

The accurate visualization and modeling of nanostructures, such as carbon nanotubes (CNTs) and graphene play a crucial role in advancing nanoscale research and applications. The versatile properties of graphene and CNTs make them fashionable among the scientific community. Modeling and simulation play an important role in understanding and predicting the behavior of these nanomaterials. Traditional modeling approaches often rely on specialized software and complex computational methods. However, the advent of versatile programming languages like Python has opened new avenues for simulating and visualizing nanostructures. Python’s extensive libraries and user-friendly syntax make it an attractive tool for researchers aiming to model CNTs and graphene structures. The study demonstrated 3D visualization of graphene and two configurations of CNTs, namely single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) marvelously using Python-based libraries. The Python codes of 3D nanostructure models advance the nanoscale research.

Method

The present study investigates the potential and limitations of Python-based libraries with a primary focus on Mayavi and PyVista for rendering and analyzing these nanostructures, while Mayavi offers a high-level 3D visualization interface built on visualization toolkit (VTK). The attempts to generate structurally accurate CNTs and graphene lattices revealed several critical challenges, including improper bonding representation, lattice distortions, and scaling inconsistencies. Moreover, the study presents the implementation strategies, code structure, and visual output limitations encountered during the modeling process. To provide a comparative landscape, additional tools such as Matplotlib (for 2D visualization), VPython (for quick atomic modeling), and atomic simulation environment (ASE) are briefly evaluated. The study also describes the role of NumPy, SciPy, and Pymatgen in computational geometry, structural logic, and periodic boundary condition (PBC) modeling.

Graphical Abstract