Classification of crystal structures using electron diffraction patterns with a deep convolutional neural network-Reference-Cited by-同舟云学术

Classification of crystal structures using electron diffraction patterns with a deep convolutional neural network

Published:2021 Issue:61 Volume:11 Page:38307-38315
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Ra Moonsoo¹^ORCID,Boo Younggun¹,Jeong Jae Min²,Batts-Etseg Jargalsaikhan²,Jeong Jinha¹³,Lee Woong²^ORCID

Affiliation:

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

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

3. TRIZ Center, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea

Abstract

The off-the-shelf deep convolutional neural network architecture, ResNet, could classify the space group of materials with cubic crystal structures with the prediction accuracy of 92.607%, using the selected area electron diffraction patterns.

Funder

Hanyang University

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/RA/D1RA07156D

Reference42 articles.

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3. B.Fultz and J. M.Howe , Transmission Electron Microscopy and Diffractometry of Materials , Springer , Berlin , 3rd edn, 2012