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Fermionic neural-network states for ab-initio electronic structure

Author

Listed:
  • Kenny Choo

    (University of Zurich)

  • Antonio Mezzacapo

    (IBM Thomas J. Watson Research Center)

  • Giuseppe Carleo

    (Flatiron Institute)

Abstract

Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems. Despite a great deal of general methodological developments, representing fermionic matter is however still early research activity. Here we present an extension of neural-network quantum states to model interacting fermionic problems. Borrowing techniques from quantum simulation, we directly map fermionic degrees of freedom to spin ones, and then use neural-network quantum states to perform electronic structure calculations. For several diatomic molecules in a minimal basis set, we benchmark our approach against widely used coupled cluster methods, as well as many-body variational states. On some test molecules, we systematically improve upon coupled cluster methods and Jastrow wave functions, reaching chemical accuracy or better. Finally, we discuss routes for future developments and improvements of the methods presented.

Suggested Citation

  • Kenny Choo & Antonio Mezzacapo & Giuseppe Carleo, 2020. "Fermionic neural-network states for ab-initio electronic structure," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15724-9
    DOI: 10.1038/s41467-020-15724-9
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    Cited by:

    1. Lennart Dabelow & Masahito Ueda, 2022. "Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Jannes Nys & Gabriel Pescia & Alessandro Sinibaldi & Giuseppe Carleo, 2024. "Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Laura Lewis & Hsin-Yuan Huang & Viet T. Tran & Sebastian Lehner & Richard Kueng & John Preskill, 2024. "Improved machine learning algorithm for predicting ground state properties," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    4. Xiang Li & Zhe Li & Ji Chen, 2022. "Ab initio calculation of real solids via neural network ansatz," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

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