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The valley transport properties of a superlattice of out-of-plane Gaussiansdeformations are calculated using a Green's function and a Machine Learningapproach. Our results show that periodicity significantly improves the valleyfilter capabilities of a single Gaussian deformation, these manifest themselvesin the conductance as a sequence by valley filter plateaus. We establish thatthe physical effect behind the observed valley notch filter is the couplingbetween counter-propagating transverse modes; the complex relationship betweenthe design parameters of the superlattice and the valley filter effect makedifficult to estimate in advance the valley filter potentialities of a givensuperlattice. With this in mind, we show that a Deep Neural Network can betrained to predict valley polarization with a precision similar to the Green'sfunction but with much less computational effort.

Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach