ArticleNature communications2024
Neural network variational Monte Carlo for positronic chemistry.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Two protons, two positrons, and four electrons: Covalent bond with van der Waals characteristics.Science advances · 2026Article
- Watch out electrons!: positron binding redefines chemical bonding in BeChemical science · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Capturing Correlation Effects in Positron Binding to Atoms and Molecules.Journal of chemical theory and computation · 2024Article
- Neural network variational Monte Carlo for positronic chemistry.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
Identifiers
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Registered trials
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