Water isotope separation, specifically separating heavy from light water, is a technologically important problem due to the usage of heavy water in applications such as nuclear magnetic resonance, nuclear power, and spectroscopy. Separation of heavy water from light water is difficult due to very similar physical and chemical properties between the isotopes. We show that a catalytically active ultrathin membrane (e.g., a nanopore in MoS₂) can enable chemical exchange processes and physicochemical mechanisms that lead to efficient separation of deuterium from hydrogen. The separation process is inherently multiscale in nature with the shorter times representing chemical exchange processes and the longer timescales representing the transport phenomena. To bridge the timescales, we employ a deep learning methodology which uses short time scale ab initio molecular dynamics data for training and extends the timescales to the classical molecular dynamics regime to demonstrate isotope separation and reveal the underlying complex physicochemical processes.

中文翻译：

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使用催化活性超薄膜进行水同位素分离的数据驱动分子动力学模拟


水同位素分离，特别是重水与轻水的分离，是一个技术上重要的问题，因为在核磁共振、核能和光谱学等应用中使用重水。由于同位素之间的物理和化学性质非常相似，因此很难分离重水和轻水。我们表明，具有催化活性的超薄膜（例如，MoS₂ 中的纳米孔）可以实现化学交换过程和物理化学机制，从而实现氘与氢的有效分离。分离过程本质上是多尺度的，较短的时间代表化学交换过程，较长的时间尺度代表运输现象。为了弥合时间尺度，我们采用了深度学习方法，该方法使用短时间尺度的 ab initio 分子动力学数据进行训练，并将时间尺度扩展到经典分子动力学领域，以证明同位素分离并揭示潜在的复杂物理化学过程。