Workshop: Machine Learning and the Physical Sciences

HubbardNet: Efficient Predictions of the Bose-Hubbard Model Spectrum with Deep Neural Networks

Ziyan Zhu · Marios Mattheakis · Weiwei Pan · Efthimios Kaxiras


We present a deep neural network (DNN)-based model, the HubbardNet, to variationally solve for the ground state and excited state wavefunctions of the one-dimensional and two-dimensional Bose-Hubbard model on a square lattice. Using this model, we obtain the Bose-Hubbard energy spectrum as an analytic function of the Coulomb parameter, U, and the total number of particles, N, from a single training, bypassing the need to solve a new hamiltonian for each different input. We show that the DNN-parametrized solutions have excellent agreement with exact diagonalization while outperforming exact diagonalization in terms of computational scaling, suggesting that our model is promising for efficient, accurate computation of exact phase diagrams of many-body lattice hamiltonians.

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