dm-PhiSNet predicts 1-RDMs from geometries via equivariant PhiSNet with two-stage training and analytic refinement, reducing SCF iterations 49-81% on six closed-shell molecules while giving accurate one-shot energies and forces without force supervision.
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Ghostly quantum systems can have discrete non-dense energy spectra under classical stability conditions, providing counterexamples to spectral denseness.
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Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement
dm-PhiSNet predicts 1-RDMs from geometries via equivariant PhiSNet with two-stage training and analytic refinement, reducing SCF iterations 49-81% on six closed-shell molecules while giving accurate one-shot energies and forces without force supervision.
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Quantum mechanics with a ghost: Counterexamples to spectral denseness
Ghostly quantum systems can have discrete non-dense energy spectra under classical stability conditions, providing counterexamples to spectral denseness.