Abstract
Nuclear Magnetic Resonance (NMR) experiments are typically performed with predetermined pulse sequences and acquisition parameters, and are oftentimes sub-optimal for individual samples under investigation. Here we explore a class of real-time optimization methods that conducts stochastic analyses on the acquired data and in turn updates and optimizes the subsequent measurements. We show superiority of the method to static approaches, both in the efficiency and quality of data acquisition, for a wide range of experiments.
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