A Real Neural Network State for Quantum Chemistry
Author: Yangjun Wu; Xiansong Xu; Dario Poletti; Yi Fan; Chu Guo; Honghui Shang
@article{Wu2023,
abstract = {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.},
author = {Wu, Yangjun and Xu, Xiansong and Poletti, Dario and Fan, Yi and Guo, Chu and Shang, Honghui},
doi = {10.3390/math11061417},
journal = {Mathematics},
keywords = {neural network,quantum chemistry,variational monte carlo},
number = {6},
pages = {1417},
title = {A Real Neural Network State for Quantum Chemistry},
volume = {11},
year = {2023}
}