中国物理B ›› 2024, Vol. 33 ›› Issue (9): 98703-098703.doi: 10.1088/1674-1056/ad625c

所属专题： SPECIAL TOPIC — Stephen J. Pennycook: A research life in atomic-resolution STEM and EELS

• • 上一篇    

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

Wenhao Yuan(袁文浩)1, Cheng Peng(彭程)1, and Qian He(何迁)1,2,†   

  1. 1 Department of Material Science and Engineering, College of Design and Engineering, National University of Singapore, 9 Engineering Drive 1, EA #03-09, 117575, Singapore;
    2 Centre for Hydrogen Innovations, National University of Singapore, E8, 1 Engineering Drive 3, 117580, Singapore
  • 收稿日期:2024-05-28 修回日期:2024-07-08 接受日期:2024-07-12 发布日期:2024-08-30
  • 通讯作者: Qian He E-mail:heqian@nus.edu.sg
  • 基金资助:
    Project supported by National Research Foundation (NRF) Singapore, under its NRF Fellowship (Grant No. NRFNRFF11-2019-0002).

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

Wenhao Yuan(袁文浩)1, Cheng Peng(彭程)1, and Qian He(何迁)1,2,†   

  1. 1 Department of Material Science and Engineering, College of Design and Engineering, National University of Singapore, 9 Engineering Drive 1, EA #03-09, 117575, Singapore;
    2 Centre for Hydrogen Innovations, National University of Singapore, E8, 1 Engineering Drive 3, 117580, Singapore
  • Received:2024-05-28 Revised:2024-07-08 Accepted:2024-07-12 Published:2024-08-30
  • Contact: Qian He E-mail:heqian@nus.edu.sg
  • Supported by:
    Project supported by National Research Foundation (NRF) Singapore, under its NRF Fellowship (Grant No. NRFNRFF11-2019-0002).

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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-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.

关键词: large language models, high-angle annular dark field imaging, deep learning

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.

Key words: large language models, high-angle annular dark field imaging, deep learning

中图分类号:  (Electron microscopy)

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