Unveiling Twist Domains in Monolayer MoS through 4D-STEM and Unsupervised Machine Learning.
Journal:
Small methods
Published Date:
Aug 6, 2025
Abstract
Dichalcogenides, such as molybdenum disulfide (MoS), are being studied extensively due to their 2D feature and various material properties. Although crystal structures are critical for applications, conventional atomic structure analyses have a limited field of view. In this study, the crystal domains of monolayer MoS synthesized by metal-organic chemical vapor deposition (MOCVD) are analyzed using 4D scanning transmission electron microscopy (STEM) and unsupervised machine learning. Twist domains (±11°) are identified through the nonnegative matrix factorization (NMF) and hierarchical clustering of numerous (>22k) diffraction patterns from a wide field of view. Preprocessing for detecting noncentrosymmetry effectively visualizes the polarities of distinct MoS domains by highlighting the violation of Friedel's law in diffraction physics. Analyses reveal that the specimen deposited on AlO (0001) at 850 °C consists of domains measuring ≈100 nm in size and featuring many mirror-twin boundaries. The findings provide valuable insights into optimizing the MOCVD process and elucidating crystal growth mechanisms.
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