状态预测信息瓶颈
State Predictive Information Bottleneck
摘要：
理解从分子动力学（MD）模拟生成的大量高维数据的能力在很大程度上依赖于低维流形（由反应坐标或RC参数化）的知识，该知识通常区分相关的亚稳态并且捕获感兴趣的相关慢动力学。多年来，基于机器学习和人工智能的方法已经被提出来处理这种低维流形的学习，但它们经常被批评为与更传统和物理可解释的方法脱节。为了解决这些问题，在这项工作中，我们提出了一种基于深度学习的状态预测信息瓶颈（SPIB）方法来从高维分子模拟轨迹中学习RC。我们证明了分析和数值的RC学习在这种方法是如何深入连接到化学物理中的committor，并可以用来准确地识别过渡态。在这种方法中，一个关键的超参数是时间延迟，或者说算法应该预测未来多远。通过对基准系统的仔细比较，我们证明了这种超参数的选择提供了有用的控制如何粗粒度，我们希望系统的亚稳态分类。因此，我们认为这项工作代表了将基于深度学习的思想系统地应用于分子模拟的一个进步，它弥合了人工智能和传统化学物理之间的差距。
英文摘要：
The ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics (MD) simulations is heavily dependent on the knowledge of a low dimensional manifold (parameterized by a reaction coordinate or RC) that typically distinguishes between relevant metastable states and which captures the relevant slow dynamics of interest. Methods based on machine learning and artificial intelligence have been proposed over the years to deal with learning such low-dimensional manifolds, but they are often criticized for a disconnect from more traditional and physically interpretable approaches. To deal with such concerns, in this work, we propose a deep learning based State Predictive Information Bottleneck (SPIB) approach to learn the RC from high dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is deeply connected to the committor in chemical physics, and can be used to accurately identify transition states. A crucial hyperparameter in this approach is the time-delay, or how far into the future the algorithm should make predictions about. Through careful comparisons for benchmark systems, we demonstrate that this hyperparameter choice gives useful control over how coarse-grained we want the metastable state classification of the system to be. We thus believe that this work represents a step forward in systematic application of deep learning based ideas to molecular simulations in a way that bridges the gap between artificial intelligence and traditional chemical physics.
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