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

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Title
Full automation of point defect detection in transition metal dichalcogenides through a dual mode deep learning algorithm
Author(s)
Dong-Hwan Yang; Chu, Yu-Seong; Okello, Odongo Francis Ngome; Seo, Seung-Young; Gunho Moon; Kim, Kwang Ho; Moon-Ho Jo; Shin, Dongwon; Mizoguchi, Teruyasu; Yang, Sejung; Si-Young Choi
Publication Date
2024-02
Journal
MATERIALS HORIZONS, v.2024, no.3, pp.747 - 757
Publisher
ROYAL SOC CHEMISTRY
Abstract
Point defects often appear in two-dimensional (2D) materials and are mostly correlated with physical phenomena. The direct visualisation of point defects, followed by statistical inspection, is the most promising way to harness structure-modulated 2D materials. Here, we introduce a deep learning-based platform to identify the point defects in 2H-MoTe2: synergy of unit cell detection and defect classification. These processes demonstrate that segmenting the detected hexagonal cell into two unit cells elaborately cropped the unit cells: further separating a unit cell input into the Te2/Mo column part remarkably increased the defect classification accuracies. The concentrations of identified point defects were 7.16 x 1020 cm2 of Te monovacancies, 4.38 x 1019 cm2 of Te divacancies and 1.46 x 1019 cm2 of Mo monovacancies generated during an exfoliation process for TEM sample-preparation. These revealed defects correspond to the n-type character mainly originating from Te monovacancies, statistically. Our deep learning-oriented platform combined with atomic structural imaging provides the most intuitive and precise way to analyse point defects and, consequently, insight into the defect-property correlation based on deep learning in 2D materials. 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.
URI
https://pr.ibs.re.kr/handle/8788114/14795
DOI
10.1039/d3mh01500a
ISSN
2051-6347
Appears in Collections:
Center for Van der Waals Quantum Solids(반데르발스 양자 물질 연구단) > 1. Journal Papers (저널논문)
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