Constraint-aware neural networks clone known semilocal XC functionals more accurately in self-consistent calculations, transfer well from molecules to solids, and outperform unconstrained models across multiple tests.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
physics.chem-ph 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Ensemble TDDFT generalizes TDDFT and EDFT by ensemble-extending the Gross-Kohn equation and XC kernel and applying EDFT to time-dependent problems, illustrated on the 2-site Hubbard model.
citing papers explorer
-
Constraint-aware functional cloning for stable and transferable machine-learned density functional theory
Constraint-aware neural networks clone known semilocal XC functionals more accurately in self-consistent calculations, transfer well from molecules to solids, and outperform unconstrained models across multiple tests.
-
Ensemble Time-Dependent Density Functional Theory
Ensemble TDDFT generalizes TDDFT and EDFT by ensemble-extending the Gross-Kohn equation and XC kernel and applying EDFT to time-dependent problems, illustrated on the 2-site Hubbard model.