Performance of a deep convolutional neural network to classify crystal structures using selected area electron beam diffraction patterns containing lattice defect information-Reference-Cited by-同舟云学术

Performance of a deep convolutional neural network to classify crystal structures using selected area electron beam diffraction patterns containing lattice defect information

Published:2024 Issue:26 Volume:14 Page:18489-18500
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Jeong Jae Min¹^ORCID,Ra Moonsoo²^ORCID,Jeong Jinha²,Lee Woong¹³^ORCID

Affiliation:

1. Dept. of Materials Convergence and System Engineering, Changwon National University, 20 Changwondaehak-ro, Changwon-si, Gyeongsangnam-do 51140, Republic of Korea

2. LightVision Inc., 20 Seongsuil-ro 12-gil, Seongdong-gu, Seoul 04793, Republic of Korea

3. School of Materials Science and Engineering, Changwon National University, 20 Changwondaehak-ro, Changwon-si, Gyeongsangnam-do 51140, Republic of Korea

Abstract

A pre-trained ResNet model was verified to have the capability of classifying crystal structures using electron diffraction patterns from crystals having defects, but with some performance decrease.

Funder

Institute for Information and Communications Technology Promotion

Ministry of Science and ICT, South Korea

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RA/D3RA08939H

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