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.

翻译：理解复杂分子过程的动力学通常与对长寿命稳态之间罕见转变的研究相关联。对此类稀有事件进行采样的标准方法是通过轨迹空间中的随机游走生成转变路径集合。然而，这种方法存在后续采样路径之间强相关性以及并行化采样过程的内在困难。我们提出了一种基于神经网络生成构型的转变路径采样方案。这些构型通过归一化流获得——这是一类能够从给定分布生成统计独立样本的神经网络。采用此方法不仅消除了访问路径之间的相关性，还使采样过程易于并行化。此外，通过条件化归一化流，可将构型采样导向感兴趣的区域。我们证明，该方法能够解析过渡区域的热力学和动力学特性。