RLEASE is a reinforcement-learning method that learns a neural-network policy to select compact, geometry-dependent active spaces for multireference calculations, trained on a small set of molecules and shown to transfer without retraining.
Multiconfiguration Self-Consistent Field and Multireference Configuration Interaction Methods and Applications , volume =
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ADC-G3W2 reformulates vertex corrections to the GW self-energy as nonperturbative resummations within the ADC framework to guarantee positive semi-definiteness of the self-energy.
citing papers explorer
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RLEASE: Reinforcement Learning Efficient Active Space Engine
RLEASE is a reinforcement-learning method that learns a neural-network policy to select compact, geometry-dependent active spaces for multireference calculations, trained on a small set of molecules and shown to transfer without retraining.
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An Algebraic-Diagrammatic Construction for Vertex Corrections to the $GW$ Self-Energy
ADC-G3W2 reformulates vertex corrections to the GW self-energy as nonperturbative resummations within the ADC framework to guarantee positive semi-definiteness of the self-energy.