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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A stochastic MCMC sampling method with umbrella sampling provides unbiased loop corrections to belief propagation for exact factorization-based tensor network contraction on loopy graphs with symmetric potentials.
citing papers explorer
-
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.
-
Stochastic Loop Corrections to Belief Propagation for Tensor Network Contraction
A stochastic MCMC sampling method with umbrella sampling provides unbiased loop corrections to belief propagation for exact factorization-based tensor network contraction on loopy graphs with symmetric potentials.