A large language model-powered literature review for high-angle annular dark field imaging

作     者：Wenhao Yuan Cheng Peng Qian He 袁文浩;彭程;何迁

作者机构：Department of Material Science and EngineeringCollege of Design and EngineeringNational University of Singapore9 Engineering Drive 1EA#03-09117575Singapore Centre for Hydrogen InnovationsNational University of SingaporeE81 Engineering Drive 3117580Singapore 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2024年第33卷第9期

页      面：76-81页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080401[工学-精密仪器及机械] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0802[工学-机械工程] 0803[工学-光学工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Research Foundation(NRF)Singapore under its NRF Fellowship(Grant No.NRFNRFF11-2019-0002) 

主　　题：large language models high-angle annular dark field imaging deep learning 

摘      要：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-°A resolution and provide chemical information through *** study leverages large language models(LLMs)to conduct a comprehensive bibliometric analysis of a large amount of HAADF-related literature(more than 41000 papers).By using LLMs,specifically ChatGPT,we were able to extract detailed information on applications,sample preparation methods,instruments used,and study *** findings highlight the capability of LLMs to provide a new perspective into HAADF imaging,underscoring its increasingly important role in materials ***,the rich information extracted from these publications can be harnessed to develop AI models that enhance the automation and intelligence of electron microscopes.