  The valley transport properties of a superlattice of out-of-plane Gaussians
deformations are calculated using a Green's function and a Machine Learning
approach. Our results show that periodicity significantly improves the valley
filter capabilities of a single Gaussian deformation, these manifest themselves
in the conductance as a sequence by valley filter plateaus. We establish that
the physical effect behind the observed valley notch filter is the coupling
between counter-propagating transverse modes; the complex relationship between
the design parameters of the superlattice and the valley filter effect make
difficult to estimate in advance the valley filter potentialities of a given
superlattice. With this in mind, we show that a Deep Neural Network can be
trained to predict valley polarization with a precision similar to the Green's
function but with much less computational effort.
