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.
Cohen, Paula Mori-Sánchez, and Weitao Yang
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The paper proposes a three-axis framework to organize hybrid quantum-classical DFT approaches and shows embedding methods suit current noisy hardware better than linear algebra speedups.
Necessary and sufficient conditions are established for the N-representability of the universal one-electron reduced density matrix functional, guaranteeing variational upper bounds on the true energy regardless of interparticle repulsion strength.
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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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Hybrid Quantum-Classical Density Functional Theory: A Structured Framework
The paper proposes a three-axis framework to organize hybrid quantum-classical DFT approaches and shows embedding methods suit current noisy hardware better than linear algebra speedups.
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Necessary and sufficient conditions for the N-representability of functionals of the one-electron reduced density matrix
Necessary and sufficient conditions are established for the N-representability of the universal one-electron reduced density matrix functional, guaranteeing variational upper bounds on the true energy regardless of interparticle repulsion strength.