2018/03/06 by Xiaoyang Lin, Zhizhong Si, Wenzhi Fu +9 · 2 citations
Materials Science · Physics and Astronomy · #2D Materials and Applications #Graphene research and applications #Identification (biology) #Machine Learning in Materials Science #Microscopy #Nanostructure #Optical microscope #Stacking #cond-mat.mtrl-sci
paper · pdf · doi:10.1007/s12274-018-2155-0
published as Nano Research, 2018, 11(12):6316-6324
arxiv created 2018/03/06 · openalex created_date 2018/03/29 · openalex publication_date 2018/08/07 · arxiv updated 2019/01/23 · openalex updated_date 2026/08/05
Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large-area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine-learning strategy in the optical identification of 2D nanostructures. The machine-learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large-area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer-scale device applications of 2D nanostructures.