Full automation of point defect detection in transition metal dichalcogenides through a dual mode deep learning algorithm

Author:

Yang Dong-Hwan12ORCID,Chu Yu-Seong3,Okello Odongo Francis Ngome1,Seo Seung-Young1,Moon Gunho12,Kim Kwang Ho4ORCID,Jo Moon-Ho12,Shin Dongwon5ORCID,Mizoguchi Teruyasu6ORCID,Yang Sejung7,Choi Si-Young128ORCID

Affiliation:

1. Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang 37673, Republic of Korea

2. Center for van der Waals Quantum Solids, Institute of Basic Science (IBS), 77 Cheongam-Ro, Pohang 37673, Republic of Korea

3. Division of Biomedical Engineering, College of Health Sciences, Yonsei University, 1, Yeonsedae-gil, Heungeop-myeon, Wonju-si, Gangwon-do, 26493, Republic of Korea

4. Department of Materials Science and Engineering, Pusan National University (PNU), 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, 46241, Busan, Republic of Korea

5. Materials Science and Technology Division, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37831, USA

6. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 15308505, Japan

7. Department of Precision Medicine, Yonsei University Wonju College of Medicine, 20, Ilsan-ro, Wonju-si, Gangwon-do, Republic of Korea

8. Department of Semiconductor Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Pohang, 37673, Republic of Korea

Abstract

We advocate for the development of expertise in visualizing and identifying point defects in two-dimensional (2D) materials, a skillset intimately linked to a wide array of physical phenomena.

Funder

Ministry of Education

Ministry of Science and ICT, South Korea

Institute for Basic Science

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrical and Electronic Engineering,Process Chemistry and Technology,Mechanics of Materials,General Materials Science

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