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Area of Science:

  • Quantum Chemistry
  • Computational Physics
  • Materials Science

Background:

  • Calculating properties of positron-molecule complexes is difficult due to basis set limitations.
  • Positron interactions with molecules are crucial for understanding annihilation processes and material properties.

Purpose of the Study:

  • To develop and validate a novel method for accurate quantum chemical calculations of positron-molecule complexes.
  • To assess the performance of the Fermionic Neural Network (FermiNet) wavefunction for these challenging systems.

Main Methods:

  • Utilized the Fermionic Neural Network (FermiNet) wavefunction, a basis-set-independent approach.
  • Performed quantum chemical calculations for ground-state properties of various atoms and small molecules with positrons.
  • Calculated binding energies and annihilation rates for positron-molecule systems.

Main Results:

  • FermiNet achieved highly accurate, state-of-the-art ground-state energies for diverse positron-molecule systems.
  • Accurate binding energy for positron-benzene complex calculated, showing good agreement with experimental data.
  • Annihilation rates computed by FermiNet favorably compared with results from explicitly correlated Gaussian wavefunctions.

Conclusions:

  • The FermiNet wavefunction offers a significant advancement for quantum chemical calculations of positron-molecule interactions.
  • Basis-set-independent neural network methods provide a versatile and accurate approach for complex quantum systems.
  • This work expands the applicability of neural network wavefunctions beyond standard molecular Hamiltonians.