Simulated HRTEM images of nanoparticles to train a neural network to classify nanoparticles for crystallinity
Author:
Affiliation:
1. Inorganic Chemistry, Centre for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, 45117 Essen, Germany
2. Ernst-Ruska Centre for Microscopy and Spectroscopy with Electrons, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
Abstract
Funder
Deutsche Forschungsgemeinschaft
Publisher
Royal Society of Chemistry (RSC)
Link
http://pubs.rsc.org/en/content/articlepdf/2024/NA/D4NA00266K
Reference50 articles.
1. OUP accepted manuscript
2. Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
3. Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise
4. In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials
5. DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time
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