STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS2
DC Field | Value | Language |
---|---|---|
dc.contributor.author | kihyun Lee | - |
dc.contributor.author | JINSUB PARK | - |
dc.contributor.author | Choi Soyeon | - |
dc.contributor.author | YANGJIN LEE | - |
dc.contributor.author | 이솔 | - |
dc.contributor.author | Jung Joowon | - |
dc.contributor.author | Lee Jong-Young | - |
dc.contributor.author | Ullah Farman | - |
dc.contributor.author | Tahir Zeeshan | - |
dc.contributor.author | Kim Yong Soo | - |
dc.contributor.author | Lee Gwan-Hyoung | - |
dc.contributor.author | KWANPYO KIM | - |
dc.date.accessioned | 2022-07-18T03:40:08Z | - |
dc.date.available | 2022-07-18T03:40:08Z | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 1530-6984 | - |
dc.identifier.uri | https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6303 | - |
dc.description.abstract | Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here, we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of MoS2 from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | AMER CHEMICAL SOC | - |
dc.title | STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS2 | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1021/acs.nanolett.2c00550 | - |
dc.identifier.scopusid | 2-s2.0-85132453474 | - |
dc.identifier.wosid | 000815203800001 | - |
dc.identifier.bibliographicCitation | NANO LETTERS, v.22, no.12, pp 4,677 - 4,685 | - |
dc.citation.title | NANO LETTERS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 4,677 | - |
dc.citation.endPage | 4,685 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | TEM image analysis | - |
dc.subject.keywordAuthor | Molybdenum disulfide | - |
dc.subject.keywordAuthor | Defect | - |
dc.subject.keywordAuthor | Polymorph | - |
Items in Scholar Hub are protected by copyright, with all rights reserved, unless otherwise indicated.
Yonsei University 50 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea1599-1885
© 2021 YONSEI UNIV. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.