First analytic nuclear gradients derived and implemented for BSE@G0W0, validated on excited-state geometries and adiabatic energies against wavefunction benchmarks.
Runge \ and\ author E
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OrbEvo uses equivariant graph transformers to learn the time evolution of TDDFT wavefunction coefficients, accurately reproducing wavefunctions, dipole moments, and absorption spectra on QM9 and MD17 molecular datasets.
Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.
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.
The paper establishes an exact N-centered ensemble DFT formalism unifying neutral and charged excitations and introduces three practical strategies: weight-dependent scaling of ground-state functionals, quasi-degenerate ensemble perturbation theory, and quantum bath embedding for excited states.
i-DFT computes spectral and transmission properties of correlated quantum dots from Coulomb blockade to Kondo regimes, matching many-body results at reduced cost.
A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.
citing papers explorer
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Fully Analytic Nuclear Gradients for the Bethe--Salpeter Equation
First analytic nuclear gradients derived and implemented for BSE@G0W0, validated on excited-state geometries and adiabatic energies against wavefunction benchmarks.
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Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory
OrbEvo uses equivariant graph transformers to learn the time evolution of TDDFT wavefunction coefficients, accurately reproducing wavefunctions, dipole moments, and absorption spectra on QM9 and MD17 molecular datasets.
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Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.
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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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Ensemble density functional theory of excited states: Exact N-centered formalism and practical opportunities
The paper establishes an exact N-centered ensemble DFT formalism unifying neutral and charged excitations and introduces three practical strategies: weight-dependent scaling of ground-state functionals, quasi-degenerate ensemble perturbation theory, and quantum bath embedding for excited states.
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Spectral and transmission properties of multiple correlated quantum dots made simple
i-DFT computes spectral and transmission properties of correlated quantum dots from Coulomb blockade to Kondo regimes, matching many-body results at reduced cost.
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Solving the Gross-Pitaevskii equation on multiple different scales using the quantics tensor train representation
A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.