| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
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
| Organizations | Location | People |
|---|
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.