  Metasurfaces are sub-wavelength patterned layers for controlling waves in
physical systems. In optics, meta-surfaces are created by materials with
different dielectric constants and are capable of unconventional
functionalities. We develop a deep-learning framework for Dirac-material
metasurface design for controlling electronic waves. The metasurface is a
configuration of circular graphene quantum dots, each created by an electric
potential. Employing deep convolutional neural networks, we show that the
original scattering wave can be reconstructed with fidelity over 95$\%$,
suggesting the feasibility of Dirac electron holography. Additional
applications such as plane wave generation, designing broadband, and
multi-functionality graphene metasurface systems are illustrated.
