COMPUTATIONAL APPROACHES FOR IDENTIFICATION OF ATOMIC STRUCTURES IN MICROSCOPY DATA

Abstract

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 thesis, 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 investigate advanced data analysis methods and computational models that have demonstrated promising prospects in analyzing STEM data, particularly for identifying structural defects, with high throughput and veracity.