A Real Neural Network State for Quantum Chemistry

Yangjun Wu, Xiansong Xu, Dario Poletti, Yi Fan, Chu Guo () and Honghui Shang ()
Additional contact information
Yangjun Wu: Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Xiansong Xu: Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
Dario Poletti: Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
Yi Fan: Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
Chu Guo: Henan Key Laboratory of Quantum Information and Cryptography, Zhengzhou 450000, China
Honghui Shang: Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Mathematics, 2023, vol. 11, issue 6, 1-10

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.

Keywords: neural network; variational Monte Carlo; quantum chemistry (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/6/1417/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/6/1417/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:6:p:1417-:d:1097915

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().