arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1802.03008cond-mat.mtrl-sci
收藏

一种识别原子分辨率透射电子显微镜图像中局部结构的深度学习方法

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

摘要:

记录原子分辨率的透射电子显微镜(TEM)图像正变得越来越常规。然后,一个新的瓶颈是分析这些信息,这通常涉及耗时的手动结构识别。我们开发了一种基于深度学习的算法,用于识别TEM图像中的局部结构,该算法对显微镜参数和噪声稳定。神经网络完全通过模拟训练,但能够对实验图像进行可靠的预测。我们将该方法应用于单层有缺陷的石墨烯,并在氧化物载体上的金属纳米颗粒。

英文摘要:

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. We have developed a deep learning-based algorithm for recognition of the local structure in TEM images, 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. We apply the method to single sheets of defected graphene, and to metallic nanoparticles on an oxide support.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑