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 MoS2 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.
|Number of pages||9|
|Publication status||Published - 2022 Jun 22|
Bibliographical noteFunding Information:
We thank Prof. Hwidong Yoo for providing computing resources (YHEP server) and helpful comments and discussions. We also thank Prof. Sejung Yang for her insightful comments and advice on data preprocessing and machine learning. This work was mainly supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF-2017R1A5A1014862 and 2022R1A2C4002559) and by the Institute for Basic Science (IBS-R026-D1). Y.L. received support from the Basic Science Research Program at the National Research Foundation of Korea which was funded by the Ministry of Science and ICT (NRF-2021R1C1C2006785). Y.S.K. acknowledges support from the Priority Research Centers Program (2019R1A6A1A11053838) and the Basic Science Research Programs (2021R1A2C1004209) through the National Research Foundation of Korea (NRF). G.H.L acknowledges the support from Creative-Pioneering Researchers Program through Seoul National University (SNU).
© 2022 The Authors. Published by American Chemical Society.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Condensed Matter Physics
- Mechanical Engineering