用于罕见事件采样的条件归一化流
Conditioning Normalizing Flows for Rare Event Sampling
AI总结：
针对罕见事件采样中路径相关性强且难以并行化的问题，提出基于条件归一化流的跃迁路径采样方案，实现统计独立样本生成与采样过程并行化，并能解析跃迁区域的热力学与动力学。
摘要：
理解复杂分子过程的动力学通常与研究长寿命稳定态之间不频繁的跃迁有关。此类罕见事件采样的标准方法是通过在轨迹空间中进行随机游走来生成跃迁路径系综。然而，这带来了随后采样的路径之间存在强相关性，以及并行化采样过程具有内在困难的缺点。我们提出了一种基于神经网络生成构型的跃迁路径采样方案。这些构型是利用归一化流获得的，归一化流是一类能够从给定分布生成统计独立样本的神经网络。通过这种方法，不仅消除了已访问路径之间的相关性，而且采样过程变得易于并行化。此外，通过对归一化流进行条件化，构型的采样可以转向感兴趣的区域。我们表明，这种方法能够解析跃迁区域的热力学和动力学。
英文摘要：
Understanding the dynamics of complex molecular processes is often linked to the study of infrequent transitions between long-lived stable states. The standard approach to the sampling of such rare events is to generate an ensemble of transition paths using a random walk in trajectory space. This, however, comes with the drawback of strong correlations between subsequently sampled paths and with an intrinsic difficulty in parallelizing the sampling process. We propose a transition path sampling scheme based on neural-network generated configurations. These are obtained employing normalizing flows, a neural network class able to generate statistically independent samples from a given distribution. With this approach, not only are correlations between visited paths removed, but the sampling process becomes easily parallelizable. Moreover, by conditioning the normalizing flow, the sampling of configurations can be steered towards regions of interest. We show that this approach enables the resolution of both the thermodynamics and kinetics of the transition region.
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