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A Real Neural Network State for Quantum Chemistry

Author

Listed:
  • 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
    College of Physics and Electronic Engineering, and Center for Computational Sciences, Sichuan Normal University, Chengdu 610068, China)

  • Dario Poletti

    (Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
    EPD Pillar, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
    MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit, Singapore UMI 3654, 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
    Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University, Changsha 410081, China)

  • Honghui Shang

    (Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China)

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.

Suggested Citation

  • Yangjun Wu & Xiansong Xu & Dario Poletti & Yi Fan & Chu Guo & Honghui Shang, 2023. "A Real Neural Network State for Quantum Chemistry," Mathematics, MDPI, vol. 11(6), pages 1-10, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1417-:d:1097915
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    Citations

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    Cited by:

    1. Laura Böhm & Sebastian Kolb & Thomas Plankenbühler & Jonas Miederer & Simon Markthaler & Jürgen Karl, 2023. "Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-25, September.
    2. Bowen Kan & Yingqi Tian & Daiyou Xie & Yangjun Wu & Yi Fan & Honghui Shang, 2024. "Solving the Electronic Schrödinger Equation by Pairing Tensor-Network State with Neural Network Quantum State," Mathematics, MDPI, vol. 12(3), pages 1-16, January.
    3. Wilson Castillo-Rojas & Fernando Medina Quispe & César Hernández, 2023. "Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks," Energies, MDPI, vol. 16(13), pages 1-25, July.

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