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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number168225
JournalJournal of Magnetism and Magnetic Materials
Volume538
DOIs
Publication statusPublished - Nov 15 2021

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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