Analysis of TEM images of metallic nanoparticles using convolutional neural networks and transfer learning

Akira Koyama, Shoko Miyauchi, Ken'ichi Morooka, Hajime Hojo, Hisahiro Einaga, Yasukazu Murakami

研究成果: ジャーナルへの寄稿学術誌査読

12 被引用数 (Scopus)

抄録

Convolutional neural networks (CNNs) pretrained by transfer learning were applied to the analysis of transmission electron microscopy (TEM) images of nanoparticles. Specifically, TEM images of non-magnetic Pt nanoparticles dispersed on a thin TiO2 crystal foil were classified using CNNs. Although the number of learning data (50≤ N≤350) was several orders of magnitude smaller than the quantities normally employed in conventional CNN analyses, the present CNN model was able to carry out image classification with 94% accuracy (average of 25 results) after the convolutional layers were pretrained by transfer learning and fine tuning. This method represents a promising tool for TEM studies of both non-magnetic and magnetic nanoparticles which make emergence of rich material functions.

本文言語英語
論文番号168225
ジャーナルJournal of Magnetism and Magnetic Materials
538
DOI
出版ステータス出版済み - 11月 15 2021

!!!All Science Journal Classification (ASJC) codes

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学

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