A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images

Abstract

Chiral indices determine important properties of carbon nanotubes (CNTs). Unfortunately, their determination from high-resolution transmission electron microscopy (HRTEM) images, the most accurate method for assigning chirality, is a tedious task. We develop a Convolutional Neural Network that automatizes this process. A large and realistic training data set of CNT images is obtained by means of atomistic computer simulations coupled with the multi-slice approach for image generation. In most cases, results of the automated assignment are in excellent agreement with manual classification, and the origin of failures is identified. The current approach, which combines HRTEM imaging and deep learning algorithms allows the analysis of a statistically significant number of HRTEM images of carbon nanotubes, paving the way for robust estimates of experimental chiral distributions.

for use of the discussed computer code, please contact the corresponding author

References in corpus (10)

• Van der Waals heterostructures
• Unusually High Thermal Conductivity of Carbon Nanotubes
• Machine learning and the physical sciences
• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images
• Raman-modes of index-identified free-standing single-walled carbon nanotubes
• Accessing high optical quality of MoS2 monolayers grown by chemical vapor deposition
• Entropy driven stability of chiral single-walled carbon nanotubes
• Chirality dependence of the absorption cross-section of carbon nanotubes
• Tuning bimetallic catalysts for a selective growth of SWCNTs

Cited by in corpus (4)

• Charge transport in semiconducting carbon nanotube networks
• Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data
• Deep learning framework for carbon nanotubes: mechanical properties and modeling strategies
• FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis