Feed-forward neural network based variational wave function for the fermionic Hubbard model in one dimension.

We explore the suitability of a feed-forward neural network (FNN) to represent the ground state of the fermionic Hubbard model in one dimension (1D). We consider the model at half-filling, represent the ground state wave function in terms of an FNN and optimize it using the variational Monte Carlo (VMC) method. The results are compared with the exact Bethe Ansatz solution.

Mott transition, magnetic and orbital orders in the ground state of the two-band Hubbard model using variational slave-spin mean field formalism.

We study the ground state phase diagram of the degenerate two-band Hubbard model at integer fillings as a function of onsite Hubbard interactionand Hund's exchange coupling. We use a variational slave-spin mean field method which allows symmetry broken states to be studied within the computationally less intensive slave-spin mean field formalism. The results show that at half-filling, the ground state at smalleris a Slater antiferromagnet with substantial local charge fluctuations.

Ground state of a three-band Hubbard model with Hund's coupling: Janus-faced behavior in presence of magnetic order.

We study the ground state of the three-band degenerate Hubbard model on a square lattice at integer fillings using the variational slave-spin mean field method. At half-filling, the method reproduces the well known result that the ground state is antiferromagnetic (AF) insulating at smaller values of Hubbard onsite repulsion, while it becomes Mott insulating with Néel AF order at higher. Away from half-filling, for two particles per site, we show that the model supports a ferromagnetic (FM) metallic state with fully polarized spins at sufficiently large.