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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A variational two-boson reduced density matrix method computes ground-state energies, densities, and correlation functions for 1D trapped bosons with contact interactions across N=2 to 10^4.
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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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Reduced density matrix approach to one-dimensional ultracold bosonic systems
A variational two-boson reduced density matrix method computes ground-state energies, densities, and correlation functions for 1D trapped bosons with contact interactions across N=2 to 10^4.