| 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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Schiøtz, Jakob
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (32/32 displayed)
- 2024Interpretability of high-resolution transmission electron microscopy imagescitations
- 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
- 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
- 2021Initiation and Progression of Anisotropic Galvanic Replacement Reactions in a Single Ag Nanowire:Implications for Nanostructure Synthesiscitations
- 2021Initiation and Progression of Anisotropic Galvanic Replacement Reactions in a Single Ag Nanowirecitations
- 2020In Situ Study of the Motion of Supported Gold Nanoparticles
- 2017Accuracy of surface strain measurements from transmission electron microscopy images of nanoparticlescitations
- 2017New Platinum Alloy Catalysts for Oxygen Electroreduction Based on Alkaline Earth Metalscitations
- 2017New Platinum Alloy Catalysts for Oxygen Electroreduction Based on Alkaline Earth Metalscitations
- 2017Nanocrystalline metals: Roughness in flatlandcitations
- 2016Exploring the Lanthanide Contraction to Tune the Activity and Stability of Pt
- 2016Exploring the Lanthanide Contraction to Tune the Activity and Stability of Pt
- 2016Correlation between diffusion barriers and alloying energy in binary alloyscitations
- 2016Pt x Gd alloy formation on Pt(111): Preparation and structural characterizationcitations
- 2015Controlling the Activity and Stability of Pt-Based Electrocatalysts By Means of the Lanthanide Contraction
- 2010Computer simulations of nanoindentation in Mg-Cu and Cu-Zr metallic glassescitations
- 2010Computer simulations of nanoindentation in Mg-Cu and Cu-Zr metallic glassescitations
- 2007Simulations of boundary migration during recrystallization using molecular dynamicscitations
- 2007Simulations of boundary migration during recrystallization using molecular dynamicscitations
- 2007An interatomic potential for studying CuZr bulk metallic glassescitations
- 2006Atomistic simulation study of the shear-band deformation mechanism in Mg-Cu metallic glassescitations
- 2004Simulation of Cu-Mg metallic glass: Thermodynamics and structurecitations
- 2004Atomistic simulations of Mg-Cu metallic glasses: Mechanical propertiescitations
- 2004Simulations of intergranular fracture in nanocrystalline molybdenumcitations
- 2003A maximum in the strength of nanocrystalline copper
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.