| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Larsen, Matthew Helmi Leth
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2024Interpretability of high-resolution transmission electron microscopy imagescitations
- 2024Beam induced heating in electron microscopy modeled with machine learning interatomic potentialscitations
- 2023Quantifying noise limitations of neural network segmentations in high-resolution transmission electron microscopycitations
- 2023Quantifying noise limitations of neural network segmentations in high-resolution transmission electron microscopycitations
- 2023Reconstructing the exit wave of 2D materials in high-resolution transmission electron microscopy using machine learningcitations
- 2022Machine-Learning Assisted Exit-wave Reconstruction for Quantitative Feature Extraction
- 2021Reconstructing the exit wave in high-resolution transmission electron microscopy using machine learningcitations
- 2021Electron beam effects in high-resolution transmission electron microscopy investigations of catalytic nanoparticles
Places of action
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article
Reconstructing the exit wave of 2D materials in high-resolution transmission electron microscopy using machine learning
Abstract
Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolutional neural networks can be used to reconstruct the exit wave from a short focal series of HRTEM images, with a fidelity comparable to conventional exit wave reconstruction. We use a fully convolutional neural network based on the U-Net architecture, and demonstrate that we can train it on simulated exit waves and simulated HRTEM images of graphene-supported molybdenum disulphide (an industrial desulfurization catalyst). We then apply the trained network to analyse experimentally obtained images from similar samples, and obtain exit waves that clearly show the atomically resolved structure of both the MoS2 nanoparticles and the graphene support. We also show that it is possible to successfully train the neural networks to reconstruct exit waves for 3400 different two-dimensional materials taken from the Computational 2D Materials Database of known and proposed two-dimensional materials.