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.
\ Hennefarth , author Valay \ Agarawal , author Leon \ Otis , author Soumi \ Haldar , author Matthew R
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2026 2verdicts
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The paper establishes an exact N-centered ensemble DFT formalism unifying neutral and charged excitations and introduces three practical strategies: weight-dependent scaling of ground-state functionals, quasi-degenerate ensemble perturbation theory, and quantum bath embedding for excited states.
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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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Ensemble density functional theory of excited states: Exact N-centered formalism and practical opportunities
The paper establishes an exact N-centered ensemble DFT formalism unifying neutral and charged excitations and introduces three practical strategies: weight-dependent scaling of ground-state functionals, quasi-degenerate ensemble perturbation theory, and quantum bath embedding for excited states.