A DF+RDMF/ACA method reduces RDMFT complexity via real-space Coulomb partitioning and adaptive bath clustering, stabilizing the bent structure of C3O2 in agreement with spectroscopy unlike PBE.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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physics.chem-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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Reducing the Complexity of Density-Matrix Functionals in a Real-Space-Decomposed DF+RDMF Scheme with the Adaptive Cluster Approximation
A DF+RDMF/ACA method reduces RDMFT complexity via real-space Coulomb partitioning and adaptive bath clustering, stabilizing the bent structure of C3O2 in agreement with spectroscopy unlike PBE.
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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.