Direct differentiation of the local energy at fixed samples yields an unbiased low-variance estimator for the variational Monte Carlo phase force in complex neural quantum states, with an adaptive mixture extending it to coupled networks and improving results on flux ladders, chiral chains, and frac
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Effective tuning of range-separated hybrid functionals supplies accurate starting orbitals for one-shot G0W0 and BSE calculations that match reference ionization potentials and neutral excitation energies across molecules and clusters.
A variational framework models SiO2 glass under pressure as binary phase coexistence and matches experimental elastic moduli and volume changes.
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Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states
Direct differentiation of the local energy at fixed samples yields an unbiased low-variance estimator for the variational Monte Carlo phase force in complex neural quantum states, with an adaptive mixture extending it to coupled networks and improving results on flux ladders, chiral chains, and frac
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Accurate starting points for one-shot $G_0W_0$ and Bethe-Salpeter Equation calculations via effective tuning of range-separated hybrid functionals
Effective tuning of range-separated hybrid functionals supplies accurate starting orbitals for one-shot G0W0 and BSE calculations that match reference ionization potentials and neutral excitation energies across molecules and clusters.
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On a variational model for phase transformation in SiO2 glass
A variational framework models SiO2 glass under pressure as binary phase coexistence and matches experimental elastic moduli and volume changes.