A Large Language Model-Powered Literature Review for HighAngle Annular Dark Field Imaging

Abstract

High-angle annular dark field (HAADF) imaging in scanning transmission electron microscopy (STEM) has become an indispensable tool in materials science due to its ability to offer sub-Å resolution and provide chemical information through Z-contrast. This study leverages large language models (LLMs) to conduct a comprehensive bibliometric analysis of a large amount of HAADF-related literature (more than 39,000 papers). By using LLMs, specifically ChatGPT, we were able to extract detailed information on applications, sample preparation methods, instrument used, and study conclusions. The findings highlight the capability of LLMs to provide a new perspective into HAADF imaging, underscoring its increasingly important role in materials science. Moreover, the rich information extracted from these publications can be harnessed to develop AI models that enhance the automation and intelligence of electron microscopes.