Basis-free neural-network geminal and Jastrow factors inside an AGP ansatz achieve sub-millihartree accuracy for H2 and rectangular H4 in VMC while exposing nodal errors at square H4 geometry.
Better, faster fermionic neural networks
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Neural wave functions uncover FFLO, polarized superfluid, phase-separated, and crystalline Cooper-pair phases in the 2D spin-imbalanced Fermi gas.
The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.
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Basis-free neural-network geminal and Jastrow factors for variational Monte Carlo
Basis-free neural-network geminal and Jastrow factors inside an AGP ansatz achieve sub-millihartree accuracy for H2 and rectangular H4 in VMC while exposing nodal errors at square H4 geometry.
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Uncovering Exotic Paired States in the 2D Spin-Imbalanced Fermi Gas with Neural Wave Functions
Neural wave functions uncover FFLO, polarized superfluid, phase-separated, and crystalline Cooper-pair phases in the 2D spin-imbalanced Fermi gas.
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Introduction to the artificial neural network-based variational Monte Carlo method
The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.
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