Valley Polarization in Two-dimensional Heterostructures Using Machine Learning (MI)

Principal Investigator
Nirpendra Singh
Department
Physics
Focus Area
Physics
Valley Polarization in Two-dimensional Heterostructures Using Machine Learning (MI)

Analogous to charge in electronics, the valley degree of freedom in the field of valleytronics constitutes the binary states and offer a tremendous advantage in data processing speeds over the electrical charge. Valleytronics has recently attracted a lot of attention where electrons carry a pseudospin that has a distinct crystal momentum and quantum valley number.

The significant separation of the crystal momentum protects the pseudospin from inter-valley scattering and leads to room temperature valley-based quantum computing and communications.

In general, it is hard to control the valley pseudospin because the valley state is not strongly coupled to any external magnetic and electric fields. The emergence of two-dimensional (2D) transition metal dichalcogenides makes it possible to control the electron’s pseudospin of the electron by lifting the valley degeneracy through breaking the time-reversal symmetry.

Valley Polarization in Two-dimensional Heterostructures Using Machine Learning (MI)