论文标题
基于深度学习的STEM图像分析：$ {MOS_2} $的空缺缺陷和多符号的识别
STEM image analysis based on deep learning: identification of vacancy defects and polymorphs of ${MoS_2}$
论文作者
论文摘要
扫描透射电子显微镜（STEM）是用于多种材料的原子分辨率结构分析的必不可少的工具。 STEM图像的常规分析是一个广泛的动手过程，它限制了高通量数据的有效处理。在这里，我们应用一个完全卷积网络（FCN）来识别二维晶体的重要结构特征。 Resunet是一种FCN的类型，用于识别来自原子分辨率STEM图像的$ {MOS_2} $的硫磺空缺和多晶型物类型。在不同水平的噪声，畸变和碳污染的情况下，基于模拟图像的训练来实现有效的模型。 FCN模型对广泛的实验茎图像的准确性与仔细的动手分析相当。我们的工作提供了有关最佳实践的指南，以训练深度学习模型进行STEM图像分析，并证明FCN有效地处理大量STEM数据。
Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of ${MoS_2}$ from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data.
