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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A structure-preserving low-rank factorization of 2RDMs achieves linear rank scaling with system size and ~99% compression while retaining chemical accuracy for correlated states.
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.
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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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Low-rank compression of two-electron reduced density matrices
A structure-preserving low-rank factorization of 2RDMs achieves linear rank scaling with system size and ~99% compression while retaining chemical accuracy for correlated states.
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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.