A Deep Learning Approach to Identify Local Structures in Atomic-Resolution Transmission Electron Microscopy Images

Jacob Madsen, Pei Liu, Jens Kling, Jakob Birkedal Wagner, Thomas Willum Hansen, Ole Winther, Jakob Schiøtz*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Article number1800037
JournalAdvanced Theory and Simulations
Issue number8
Number of pages12
Publication statusPublished - 2018

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