Convolutional restricted Boltzmann machine (CRBM) correlated variational wave function for the Hubbard model on a square lattice: Mott metal-insulator transition

Understand

We use a convolutional restricted Boltzmann machine (CRBM) neural network to construct a variational wave function (WF) for the Hubbard model on a square lattice and study it using the variational Monte Carlo (VMC) method.

• In the wave function, the CRBM acts as a correlation factor to a mean-field BCS state.
• The number of variational parameters in the WF does not grow automatically with the lattice size and it is computationally much more efficient compared to other neural network based WFs.
• We find that in the intermediate to strong coupling regime of the model at half-filling, the wave function outperforms even the highly accurate long range backflow-Jastrow correlated wave function.