  Two-dimensional (2D) materials and their heterostructures, with wafer-scale
synthesis methods and fascinating properties, have attracted numerous interest
and triggered revolutions of corresponding device applications. However, facile
methods to realize accurate, intelligent and large-area characterizations of
these 2D structures are still highly desired. Here, we report a successful
application of machine-learning strategy in the optical identification of 2D
structure. The machine-learning optical identification method (MOI method)
endows optical microscopy with intelligent insight into the characteristic
colour information in the optical photograph. Experimental results indicate
that the MOI method enables accurate, intelligent and large-area
characterizations of graphene, molybdenum disulphide (MoS2) and their
heterostructures, including identifications of the thickness, the existence of
impurities, and even the stacking order. Thanks to the convergence of
artificial intelligence and nanoscience, this intelligent identification method
can certainly promote the fundamental research and wafer-scale device
application of 2D structures.
