We present a method using Zernike moments for quantifying rotational and reflectional symmetries in scanning transmission electron microscopy(STEM)images,aimed at improving structural analysis of materials at the atom...We present a method using Zernike moments for quantifying rotational and reflectional symmetries in scanning transmission electron microscopy(STEM)images,aimed at improving structural analysis of materials at the atomic scale.This technique is effective against common imaging noises and is potentially suited for low-dose imaging and identifying quantum defects.We showcase its utility in the unsupervised segmentation of polytypes in a twisted bilayer TaS_(2),enabling accurate differentiation of structural phases and monitoring transitions caused by electron beam effects.This approach enhances the analysis of structural variations in crystalline materials,marking a notable advancement in the characterization of structures in materials science.展开更多
Enabled by the advances in aberration-corrected scanning transmission electron microscopy(STEM),atomic-resolution real space imaging of materials has allowed a direct structure-property investigation.Traditional ways ...Enabled by the advances in aberration-corrected scanning transmission electron microscopy(STEM),atomic-resolution real space imaging of materials has allowed a direct structure-property investigation.Traditional ways of quantitative data analysis suffer from low yield and poor accuracy.New ideas in the field of computer vision and machine learning have provided more momentum to harness the wealth of big data and sophisticated information in STEM data analytics,which has transformed STEM from a localized characterization technique to a macroscopic tool with intelligence.In this review article,we discuss the prime significance of defect topology and density in two-dimensional(2D)materials,which have proved to be a powerful means to tune a wide range of properties.Subsequently,we systematically review advanced data analysis methods that have demonstrated promising prospects in analyzing STEM data,particularly for identifying structural defects,with high throughput and veracity.A unified framework for atomic structure identification is also summarized.展开更多
基金funding support from the National Research Foundation (Competitive Research Program grant number NRF-CRP16-2015-05)the National University of Singapore Early Career Research Award+1 种基金supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowshipa Schmidt Sciences program。
文摘We present a method using Zernike moments for quantifying rotational and reflectional symmetries in scanning transmission electron microscopy(STEM)images,aimed at improving structural analysis of materials at the atomic scale.This technique is effective against common imaging noises and is potentially suited for low-dose imaging and identifying quantum defects.We showcase its utility in the unsupervised segmentation of polytypes in a twisted bilayer TaS_(2),enabling accurate differentiation of structural phases and monitoring transitions caused by electron beam effects.This approach enhances the analysis of structural variations in crystalline materials,marking a notable advancement in the characterization of structures in materials science.
基金Support by the Singapore Ministry of Education through a Tier 2 grant(MOE2017-T2-2-139)is gratefully acknowledged。
文摘Enabled by the advances in aberration-corrected scanning transmission electron microscopy(STEM),atomic-resolution real space imaging of materials has allowed a direct structure-property investigation.Traditional ways of quantitative data analysis suffer from low yield and poor accuracy.New ideas in the field of computer vision and machine learning have provided more momentum to harness the wealth of big data and sophisticated information in STEM data analytics,which has transformed STEM from a localized characterization technique to a macroscopic tool with intelligence.In this review article,we discuss the prime significance of defect topology and density in two-dimensional(2D)materials,which have proved to be a powerful means to tune a wide range of properties.Subsequently,we systematically review advanced data analysis methods that have demonstrated promising prospects in analyzing STEM data,particularly for identifying structural defects,with high throughput and veracity.A unified framework for atomic structure identification is also summarized.
