AI-driven image processing framework for high-accuracy detection and characterization of vacancies in 2D materials

Two-dimensional (2D) materials such as graphene, phosphorene, and MoS₂ offer transformative opportunities but are often impaired by synthesis-induced vacancies. Existing characterization tools—atomic-resolution microscopy and molecular dynamics—are slow, costly, and unsuitable for large-scale screening. We introduce an AI-driven image-processing framework that combines automated grayscale image analysis with machine learning to rapidly and accurately detect and quantify vacancies. By converting structural information into tailored pixel-based descriptors, our method regresses defect coordinates, radii, and densities without atomic-scale input. Validated on thousands of simulated images with controlled defects, the framework attains over 96% prediction accuracy and surpasses thermal vibration analysis (~90%) while removing the need for specialized experimental setups. The approach is generalizable across material systems, enabling high-throughput screening and standardized defect analysis for nanoelectronics, sensing, and quantum technology applications. This work accelerates the AI-guided design and optimization of defect-engineered 2D materials.