STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS 2 .
Kihyun LeeJinsub ParkSoyeon ChoiYangjin LeeSol LeeJoowon JungJong-Young LeeFarman UllahZeeshan TahirYong Soo KimGwan-Hyoung LeeKwanpyo KimPublished in: Nano letters (2022)
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.
Keyphrases
- deep learning
- convolutional neural network
- electron microscopy
- high throughput
- artificial intelligence
- healthcare
- primary care
- quantum dots
- optical coherence tomography
- electronic health record
- room temperature
- machine learning
- gene expression
- risk assessment
- big data
- high resolution
- mass spectrometry
- single molecule
- air pollution
- data analysis
- climate change
- high speed
- drinking water
- highly efficient