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Synthetic Stochastic Time Series from Generative Models
Obtaining statistically converged time-series data represents a fundamental challenge in computational chemistry, where sampling is constrained by the prohibitive computational cost of high-fidelity simulations. The accuracy attainable from a finite simulation budget is governed by the standard-erro
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