首页|A large language model-powered literature review for high-angle annular dark field imaging
A large language model-powered literature review for high-angle annular dark field imaging
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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 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 41000 papers).By using LLMs,specifically ChatGPT,we were able to extract detailed information on applications,sample preparation methods,instruments used,and study conclusions.The findings highlight the capability of LLMs to provide a new perspective into HAADF imaging,underscoring its in-creasingly important role in materials science.Moreover,the rich information extracted from these publications can be harnessed to develop Al models that enhance the automation and intelligence of electron microscopes.
large language modelshigh-angle annular dark field imagingdeep learning
袁文浩、彭程、何迁
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Department of Material Science and Engineering,College of Design and Engineering,National University of Singapore,9 Engineering Drive 1,EA #03-09,117575,Singapore
Centre for Hydrogen Innovations,National University of Singapore,E8,1 Engineering Drive 3,117580,Singapore
National Research Foundation(NRF)Singapore,under its NRF Fellowship
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NSTL
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