A Deep Learning Approach to Identify Local Structures in Atomic‐Resolution Transmission Electron Microscopy Images. Issue 8 (3rd July 2018)

• Record Type: Journal Article
• Authors: Madsen, Jacob; Liu, Pei; Kling, Jens; Wagner, Jakob Birkedal; Hansen, Thomas Willum; Winther, Ole; Schiøtz, Jakob
• Is Part Of: Advanced theory and simulations. Volume 1:Issue 8(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-03
• Subjects: atom identification -- electron microscopy -- image analysis -- neural networks; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.201800037
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Recording atomic‐resolution transmission electron microscopy (TEM) images is becoming increasingly routine. A new bottleneck is then analyzing this information, which often involves time‐consuming manual structural identification. A deep learning‐based algorithm for recognition of the local structure in TEM images was developed, which is stable to microscope parameters and noise. The neural network is trained entirely from simulation but is capable of making reliable predictions on experimental images. The method is applied to single sheets of defected graphene, and to metallic nanoparticles on an oxide support. Abstract : Neural networks can be a valuable tool for automatic analysis of high‐resolution transmission electron micrographs and image sequences . Neural networks trained entirely on simulated images can reliably identify atoms in experimental images, with a performance equal to a trained microscopist. This is demonstrated on single sheets of graphene and on metallic nanoparticles on oxide support.