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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Mapping N-queens to a lattice gas model shows that specific heat converges to a universal curve without phase transition, enabling thermodynamic integration to compute the Simkin constant γ ≈ 1.944 from Monte Carlo data alone.
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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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Statistical mechanics of the $N$-queens problem
Mapping N-queens to a lattice gas model shows that specific heat converges to a universal curve without phase transition, enabling thermodynamic integration to compute the Simkin constant γ ≈ 1.944 from Monte Carlo data alone.