Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
author Saxon, D.S
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UNVERDICTED 3representative citing papers
DFTB models for cerium allotropes were created that accurately predict band structures and energetic ordering by globally optimizing confining potentials to fit minimal DFT data and extract a many-body repulsive term.
Three standard inflationary potentials remain compatible with Planck, BICEP/Keck, DESI DR2, and ACT DR6 data when placed in minimally coupled f(R,T)=R+16πGλT gravity for suitable ranges of the model parameters and coupling λ.
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Neural-Network-Based Variational Method in Nuclear Density Functional Theory: Application to the Extended Thomas-Fermi Model
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
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Determination of Density Functional Tight Binding Models for Cerium Allotropes
DFTB models for cerium allotropes were created that accurately predict band structures and energetic ordering by globally optimizing confining potentials to fit minimal DFT data and extract a many-body repulsive term.
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Inflationary models in a minimally coupled $f(R,T)$ gravity: Constraints from $Planck$, BICEP/$Keck$, and ACT
Three standard inflationary potentials remain compatible with Planck, BICEP/Keck, DESI DR2, and ACT DR6 data when placed in minimally coupled f(R,T)=R+16πGλT gravity for suitable ranges of the model parameters and coupling λ.