MR-SCDFT augments standard multireference DFT by using stochastic fields to create reference configurations and a projection-selection step, yielding lower ground-state energies, smaller proton radii, and softer bands than conventional MR-CDFT for 20Ne, 24Mg, and 28Si.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
An emulator combining FeynmanNet with eigenvector continuation computes nuclear ground states to <0.5% error and shows that the leading-order pionless EFT cannot simultaneously fit experimental energies of 12C and 16O.
Neural-network ensembles match closed Gaussian systems but lack the open-system non-Hermitian generator and continuous spectrum required by nuclear optical models, yielding a structural negative on applicability.
citing papers explorer
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Multireference Covariant Density Functional Theory with Stochastic Basis
MR-SCDFT augments standard multireference DFT by using stochastic fields to create reference configurations and a projection-selection step, yielding lower ground-state energies, smaller proton radii, and softer bands than conventional MR-CDFT for 20Ne, 24Mg, and 28Si.
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Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation
An emulator combining FeynmanNet with eigenvector continuation computes nuclear ground states to <0.5% error and shows that the leading-order pionless EFT cannot simultaneously fit experimental energies of 12C and 16O.
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Integrating Out, Twice:The Open-System Case That Neural-Network Ensemble Theory Is Missing
Neural-network ensembles match closed Gaussian systems but lack the open-system non-Hermitian generator and continuous spectrum required by nuclear optical models, yielding a structural negative on applicability.