A deep free energy model learns the SCHA free energy surface to enable high-throughput crystal structure prediction with finite-temperature and nuclear quantum effects, reproducing known La-Sc-H phases and discovering a new stable LaScH8 structure.
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The power distribution is the target of power sampling, the closed-form solution to self-reward KL-regularized RL, and the basis for power self-distillation that matches sampling performance at lower cost.
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Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning
A deep free energy model learns the SCHA free energy surface to enable high-throughput crystal structure prediction with finite-temperature and nuclear quantum effects, reproducing known La-Sc-H phases and discovering a new stable LaScH8 structure.
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Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation
The power distribution is the target of power sampling, the closed-form solution to self-reward KL-regularized RL, and the basis for power self-distillation that matches sampling performance at lower cost.