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

doi:10.1088/1674-1056/ad625c

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 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 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.

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

Received: 28 May 2024
Revised: 08 July 2024
Accepted manuscript online: 12 July 2024

PACS:	87.64.Ee	(Electron microscopy)
	84.35.+i	(Neural networks)
	87.64.mf	(Dark field)
	01.30.-y	(Physics literature and publications)

Fund: Project supported by National Research Foundation (NRF) Singapore, under its NRF Fellowship (Grant No. NRFNRFF11-2019-0002).

Corresponding Authors: Qian He
E-mail: heqian@nus.edu.sg

Cite this article:

Wenhao Yuan(袁文浩), Cheng Peng(彭程), and Qian He(何迁) A large language model-powered literature review for high-angle annular dark field imaging 2024 Chin. Phys. B 33 098703

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

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