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

Ziyan Zhu; Marios Mattheakis; Weiwei Pan; Efthimios Kaxiras

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arXiv · 2212.13678

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

Ziyan Zhu·Marios Mattheakis·Weiwei Pan·Efthimios Kaxiras

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Abstract

We present a deep neural network (DNN)-based model (HubbardNet) to variationally find the ground state and excited state wavefunctions of the one-dimensional and two-dimensional Bose-Hubbard model. Using this model for a square lattice with $M$ sites, we obtain the energy spectrum as an analytical function of the on-site Coulomb repulsion, $U$, and the total number of particles, $N$, from a single training. This approach bypasses the need to solve a new hamiltonian for each different set of values $(U,N)$. Using \texttt{HubbardNet}, we identify the two ground state phases of the Bose-Hubbard model (Mott insulator and superfluid). We show that the DNN-parametrized solutions are in excellent agreement with results from the exact diagonalization of the hamiltonian, and it outperforms exact diagonalization in terms of computational scaling. These advantages suggest that our model is promising for efficient and accurate computation of exact phase diagrams of many-body lattice hamiltonians.

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Ziyan Zhu, Marios Mattheakis, Weiwei Pan, Efthimios Kaxiras. 2022-12-28. HubbardNet: Efficient Predictions of the Bose-Hubbard Model Spectrum with Deep Neural Networks. https://arxiv.org/abs/2212.13678

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