Neural network variational Monte Carlo for positronic chemistry

Gino Cassella (), W. M. C. Foulkes, David Pfau and James S. Spencer
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Gino Cassella: Imperial College London
W. M. C. Foulkes: Imperial College London
David Pfau: Imperial College London
James S. Spencer: DeepMind

Nature Communications, 2024, vol. 15, issue 1, 1-7

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49290-1

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