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.
Hellmann,Einf¨ uhrung in die Quantenchemie(Franz Deuticke, Leipzig, 1937)
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Generalized ML force fields reproduce non-collinear magnetic orders on lattices and predict voltage-driven domain-wall motion in itinerant magnets using extensions to nonequilibrium torques.
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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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Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
Generalized ML force fields reproduce non-collinear magnetic orders on lattices and predict voltage-driven domain-wall motion in itinerant magnets using extensions to nonequilibrium torques.