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Neural network variational Monte Carlo for positronic chemistry

Author

Listed:
  • Gino Cassella

    (Imperial College London)

  • W. M. C. Foulkes

    (Imperial College London)

  • David Pfau

    (Imperial College London
    DeepMind)

  • James S. Spencer

    (DeepMind)

Abstract

Quantum chemical calculations of the ground-state properties of positron-molecule complexes are challenging. The main difficulty lies in employing an appropriate basis set for representing the coalescence between electrons and a positron. Here, we tackle this problem with the recently developed Fermionic neural network (FermiNet) wavefunction, which does not depend on a basis set. We find that FermiNet produces highly accurate, in some cases state-of-the-art, ground-state energies across a range of atoms and small molecules with a wide variety of qualitatively distinct positron binding characteristics. We calculate the binding energy of the challenging non-polar benzene molecule, finding good agreement with the experimental value, and obtain annihilation rates which compare favourably with those obtained with explicitly correlated Gaussian wavefunctions. Our results demonstrate a generic advantage of neural network wavefunction-based methods and broaden their applicability to systems beyond the standard molecular Hamiltonian.

Suggested Citation

  • Gino Cassella & W. M. C. Foulkes & David Pfau & James S. Spencer, 2024. "Neural network variational Monte Carlo for positronic chemistry," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49290-1
    DOI: 10.1038/s41467-024-49290-1
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    References listed on IDEAS

    as
    1. Clifford M. Surko, 2007. "A whiff of antimatter soup," Nature, Nature, vol. 449(7159), pages 153-155, September.
    2. Jaroslav Hofierka & Brian Cunningham & Charlie M. Rawlins & Charles H. Patterson & Dermot G. Green, 2022. "Many-body theory of positron binding to polyatomic molecules," Nature, Nature, vol. 606(7915), pages 688-693, June.
    3. M. T. Entwistle & Z. Schätzle & P. A. Erdman & J. Hermann & F. Noé, 2023. "Electronic excited states in deep variational Monte Carlo," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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