A Real Neural Network State for Quantum Chemistry

@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}
}