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 physics-guided neural network maps density functional theory potential energy landscapes to interacting boson model parameters for rare-earth nuclei, yielding spectra that reflect structural evolution.
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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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Microscopic derivation of the interacting boson model parameters with machine learning
A physics-guided neural network maps density functional theory potential energy landscapes to interacting boson model parameters for rare-earth nuclei, yielding spectra that reflect structural evolution.