Neural network variational Monte Carlo for positronic chemistry

TL;DR

This paper demonstrates that neural network wavefunctions, specifically FermiNet, can accurately compute ground-state energies and properties of positron-molecule complexes without basis sets, outperforming traditional methods.

Contribution

The study introduces the application of FermiNet neural network wavefunctions to positronic chemistry, achieving high accuracy in ground-state energies and properties without basis set dependence.

Findings

FermiNet yields state-of-the-art energies for positron-molecule systems.

Accurate binding energies for challenging molecules like benzene.

Neural network methods outperform traditional explicitly correlated Gaussian approaches.

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.