# Deep learning automates defect detection in 2D materials **URL:** \ **Category:** News\ **Created:** [September 2, 2025, 6:56am UTC](https://community.materialssquare.com/t/deep-learning-automates-defect-detection-in-2d-materials/1202 "2025-09-02T06:56:37Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![Ahn\_Yurim](https://avatars.discourse-cdn.com/v4/letter/a/c5a1d2/32.png) [@Ahn\_Yurim](https://community.materialssquare.com/u/Ahn_Yurim)\ **Post date:** [September 2, 2025, 6:56am UTC](https://community.materialssquare.com/t/deep-learning-automates-defect-detection-in-2d-materials/1202/1 "2025-09-02T06:56:37Z")
Deep learning automation, utilizing SAM and CNN, detects MoSâ‚‚ defects in STM images with 95% accuracy. DFT simulations analyzed defect states, revealing mid-gap features. This advancement accelerates research in 2D materials and promotes defect engineering in materials science. For more details, please continue reading the full article under the following link: > **[Deep learning automates defect detection in 2D materials](https://phys.org/news/2025-08-deep-automates-defect-2d-materials.html)** > > A study published in Molecules and led by researchers from the Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) of the Chinese Academy of Sciences demonstrated how deep learning can streamline the identification of atomic-scale... The Materials Square platform in fact provides extensive web-based functionalities for computational chemistry/materials research and training/education purposes, as detailed also in the following PDF brochure: [MaterialsSquare-brochure(2024).pdf](https://community.materialssquare.com/uploads/short-url/gpMFGwPXItuGUXGHG2sA2hBJCuY.pdf) (5.0 MB) #materials #materialsscience #materialsengineering #computationalchemistry #modelling #chemistry #researchanddevelopment #research #MaterialsSquare #ComputationalChemistry #Tutorial #DFT #simulationsoftware #simulation