A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images
Year: 2020
Type: article
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.
Source: Carbon
Institutions: Centre National de la Recherche Scientifique, Aix-Marseille Université, Université Paris-Saclay, Office National d'Études et de Recherches Aérospatiales, Laboratoire d'Étude des Microstructures, Centre Interdisciplinaire de Nanoscience de Marseille, University of Pennsylvania, Université Paris Cité, Laboratoire Matériaux et Phénomènes Quantiques
Language: English
Subfield: Structural Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,700
OpenAlex ID: W3013379800