We investigate spin transport through graphene-like substrates stubbed vertically with transition-metal-dichalkogenides (TMDs). A tight-binding model is used based on a graphene-like Hamiltonian that includes different types of spin-orbit coupling (SOC) terms permitted by the C3v symmetry group in TMDs/graphene-like heterostructures. The results show a spin modulation obtained by tuning the strength and sign of the Fermi energy EF and not by varying the SOC strength as is mainly the case of Datta and Das. The spin conductance is directly controlled by the value of EF . In addition, a perfect electron-spin modulation is obtained when a vertical strain is introduced. In this case, the spin conductance exhibits a strong energy dependence. The results may open the route to a combination of graphene-like substrates with TMD stubs and the development of spin-transistor devices controlled by the Fermi energy rather than the SOC strength.
A theoretical study of artificial neural network modelling, based on vibrational dynamic data for 2D lattice, is proposed in this paper. The main purpose is to establish a neurocomputing model able to predict the 2D structures of crystal surfaces. In material surfaces, atoms can be arranged in different possibilities, defining several 2D configurations, such as triangular, square lattices, etc. To describe these structures, we usually employ the Wood notations, which are considered as the simplest manner and the most frequently used to spot the surfaces in physics. Our contribution consists to use the vibration lattice of perfect 2D structures along with the matrix and Wood notations to build up an input-output set to feed the neural model. The input data are given by the frequency modes over high symmetry points and the group velocity. The output data are given by the basis vectors corresponding to surface reconstruction and the rotation angle which aligns the unit cell of the reconstructed surface. Results showed that the method of collecting the dataset was very suitable for building a neurocomputing model that is able to predict and classify the 2D surface of the crystals. Moreover, the model was able to generate the lattice spacing for a given structure.
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