Preprint article · Journal article
A Deep Learning Approach to Identify Local Structures in Atomic-Resolution Transmission Electron Microscopy Images
Department of Physics, Technical University of Denmark1
Center for Atomic-scale Materials Design, Centers, Technical University of Denmark2
Center for Electron Nanoscopy, Technical University of Denmark3
Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark5
Recording atomic-resolution transmission electron microscopy (TEM) images isbecoming increasingly routine. A new bottleneck is then analyzing thisinformation, which often involves time-consuming manual structuralidentification. We have developed a deep learning-based algorithm forrecognition of the local structure in TEM images, which is stable to microscopeparameters and noise.
The neural network is trained entirely from simulationbut is capable of making reliable predictions on experimental images. We applythe method to single sheets of defected graphene, and to metallic nanoparticleson an oxide support.
| Language: | English |
|---|---|
| Year: | 2018 |
| Pages: | 1800037 |
| ISSN: | 25130390 |
| Types: | Preprint article and Journal article |
| DOI: | 10.1002/adts.201800037 |
| ORCIDs: | Liu, Pei , Kling, Jens , Wagner, Jakob Birkedal , Hansen, Thomas Willum , Winther, Ole and Schiøtz, Jakob |
