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 correlation between subsequently visited 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 decorrelated 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 the regions of interest. We show that this allows for resolving both the thermodynamics and kinetics of the transition region.

翻译：了解复杂的分子过程的动态往往与研究寿命长的稳定状态之间的不定期转变有关。这种稀有事件取样的标准方法是利用在轨迹空间中随机行走来产生交替路径的组合。然而,这与随后访问的路径之间的强烈关联的缺陷以及取样过程平行化的内在困难有关。我们提出基于神经网络生成的配置的过渡路径取样计划。这些计划是利用正常流、能够从特定分布中产生与装饰有关的样本的神经网络类获得的。采用这种方法,不仅在被访问路径之间取出的相关性,而且取样过程也很容易平行进行。此外,通过调整正常流,组合的取样可以引导到感兴趣的区域。我们表明,这可以解决转型区域的热动力和动能。