A Real Neural Network State for Quantum Chemistry

The restricted Boltzmann machine (RBM) has recently been demonstrated as a useful tool to solve the quantum many-body problems. In this work we propose tanh-FCN, which is a single-layer fully connected neural network adapted from RBM, to study ab initio quantum chemistry problems. Our contribution is two-fold: (1) our neural network only uses real numbers to represent the real electronic wave function, while we obtain comparable precision to RBM for various prototypical molecules; (2) we show that the knowledge of the Hartree-Fock reference state can be used to systematically accelerate the convergence of the variational Monte Carlo algorithm as well as to increase the precision of the final energy.

Automated summary

In this paper, a single-layer fully connected neural network called tanh-FCN is proposed as a tool to solve ab initio quantum chemistry problems, adapted from the restricted Boltzmann machine (RBM). The network represents real electronic wave functions using real numbers, achieving comparable precision to RBM for various molecules. Additionally, the authors show that knowledge of the Hartree-Fock reference state can be utilized to accelerate the convergence of the variational Monte Carlo algorithm and improve the energy precision.