| 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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Helveg, Stig
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (17/17 displayed)
- 2024Stable mass-selected AuTiOx nanoparticles for CO oxidationcitations
- 2024Stable mass-selected AuTiO x nanoparticles for CO oxidationcitations
- 2023Reconstructing the exit wave of 2D materials in high-resolution transmission electron microscopy using machine learningcitations
- 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
- 2022Reversible Atomization and Nano-Clustering of Pt as a Strategy for Designing Ultra-Low-Metal-Loading Catalystscitations
- 2021Reconstructing the exit wave in high-resolution transmission electron microscopy using machine learningcitations
- 2021Single-atom Pt promotion of industrial Co-Mo-S catalysts for ultra-deep hydrodesulfurizationcitations
- 2018Topotactic Growth of Edge-Terminated MoS 2 from MoO 2 Nanocrystalscitations
- 2018Topotactic Growth of Edge-Terminated MoS2 from MoO2 Nanocrystalscitations
- 2012The energies of formation and mobilities of Cu surface species on Cu and ZnO in methanol and water gas shift atmospheres studied by DFTcitations
- 2012Stability of platinum nanoparticles supported on SiO2/Si(111):a high-pressure X-ray photoelectron spectroscopy studycitations
- 2011Atomic-scale non-contact AFM studies of alumina supported nanoparticles
- 2011Stabilization Principles for Polar Surfaces of ZnOcitations
- 2006Nanotechnology / Chemical identification of point defects and adsorbates on a metal oxide surface by atomic force microscopycitations
- 2006Chemical identification of point defects and adsorbates on a metal oxide surface by atomic force microscopycitations
- 2003In situ electron energy loss spectroscopy studies of gas-dependent metal - Support interactions in Cu/ZnO catalysts
Places of action
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article
Machine-Learning Assisted Exit-wave Reconstruction for Quantitative Feature Extraction
Abstract
Reconstruction of the exit wave is a powerful tool to extract the maximal amount of information from High-resolution Transmission Electron Microscopy (HRTEM). In addition to the three-dimensional structure of the nanoparticle, the reconstructed exit waves also contained information about the beam-stimulated vibrations of the atoms nearthe edge of the nanoparticle. We have recently demonstrated that convolutional neural networks are able to reconstruct the exit wave fromafocal serieswith a low number of images. We train the neural networks on simulated images. The simulated images are produced with the multislice algorithm using the abTEM software, both the exit wave function and images produced with three different values of the defocus are saved. The neural network is then trained to reconstruct the exit wave from the images. The network is validated on a different set of simulated images, and if applicable applied to experimentally obtained data. We demonstrated that it is possible to train neural networks to reconstruct the exit wave for a varied set of samples consisting of all structures in the Computational 2D Materials Database (C2DB). For a specialized dataset such asMolybdenum Disulphide (MoS2) supported on graphene, a slightlylower error rate can be obtained(Figure 2), and realistic results can be obtained when the network is applied to experimental data. In this work, we investigate how far the convolutional neural networks can be optimized towards obtaining quantitative information from experimental data, with a particular focus on the kind of data i.e.,reconstructing exit waves with sufficient accuracy to extract the three-dimensional structure and the amplitudes of the atomic vibrations. This can be realized with more flexible training sets than in our previous publicationand by training the network to ignore the support when reconstructing the exit wave.