  A scalable tight-binding model is applied for large-scale quantum transport
calculations in clean graphene subject to electrostatic superlattice
potentials, including two types of graphene superlattices: moir\'e patterns due
to the stacking of graphene and hexagonal boron nitride (hBN) lattices, and
gate-controllable superlattices using a spatially modulated gate capacitance.
In the case of graphene/hBN moir\'e superlattices, consistency between our
transport simulation and experiment is satisfactory at zero and low magnetic
field, but breaks down at high magnetic field due to the adopted simple model
Hamiltonian that does not comprise higher-order terms of effective vector
potential and Dirac mass terms. In the case of gate-controllable superlattices,
no higher-order terms are involved, and the simulations are expected to be
numerically exact. Revisiting a recent experiment on graphene subject to a
gated square superlattice with periodicity of 35 nm, our simulations show
excellent agreement, revealing the emergence of multiple extra Dirac cones at
stronger superlattice modulation.
