A study published in Molecules, led by researchers from the Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) of the Chinese Academy of Sciences, has demonstrated how deep learning can significantly improve the detection of atomic-scale defects in molybdenum disulfide (MoS2), a key two-dimensional (2D) material with potential applications in next-generation electronic devices. Traditionally, identifying such minute defects in 2D materials has relied on time-consuming, manual analysis of high-resolution transmission electron microscopy (HRTEM) images, which is both labor-intensive and prone to human error. The research team developed a deep learning model trained on thousands of labeled HRTEM images to automatically recognize and classify various types of atomic-scale defects in MoS2, including vacancies, grain boundaries, and distortions in the crystal lattice. The model achieved high accuracy and consistency, reducing analysis time from hours to seconds while maintaining precision comparable to expert human analysts. This advancement not only accelerates materials characterization but also enables faster iteration and optimization in the development of 2D material-based technologies. The approach could be extended to other 2D materials and imaging techniques, paving the way for more efficient, scalable quality control in nanotechnology and advanced semiconductor manufacturing.