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.
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A neural network LDA functional overfit to water data achieves 1 kcal/mol errors on ionization and atomization energies and matches PBE/B3LYP on WATER27 binding energies after transfer learning from one datum.
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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.
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Overfitting by design: neural network density functionals for water
A neural network LDA functional overfit to water data achieves 1 kcal/mol errors on ionization and atomization energies and matches PBE/B3LYP on WATER27 binding energies after transfer learning from one datum.