Unveiling Twist Domains in Monolayer MoS₂ through 4D-STEM and Unsupervised Machine Learning

Dichalcogenides, such as molybdenum disulfide (MoS₂), 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 MoS₂ 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 MoS₂ domains by highlighting the violation of Friedel's law in diffraction physics. Analyses reveal that the specimen deposited on Al₂O₃ (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.