Issue 3, 2024

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

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 × 1020 cm2 of Te monovacancies, 4.38 × 1019 cm2 of Te divacancies and 1.46 × 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.

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

Supplementary files

Article information

Article type
Communication
Submitted
18 Sep 2023
Accepted
30 Oct 2023
First published
22 Nov 2023

Mater. Horiz., 2024,11, 747-757

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

D. Yang, Y. Chu, O. F. N. Okello, S. Seo, G. Moon, K. H. Kim, M. Jo, D. Shin, T. Mizoguchi, S. Yang and S. Choi, Mater. Horiz., 2024, 11, 747 DOI: 10.1039/D3MH01500A

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