Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA.
Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA.
J Chem Phys. 2021 Apr 7;154(13):134111. doi: 10.1063/5.0038198.
The ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics 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 approach to learn the RC from high-dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is 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.
从分子动力学模拟中生成的大量高维数据中理解其意义，在很大程度上取决于对低维流形（由反应坐标或 RC 参数化）的了解，该流形通常可以区分相关的亚稳状态，并捕获相关的感兴趣的慢动力学。多年来，已经提出了基于机器学习和人工智能的方法来处理学习这种低维流形的问题，但它们经常因与更传统和物理可解释的方法脱节而受到批评。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方法，从高维分子模拟轨迹中学习 RC。我们从理论和数值上证明了，在这种方法中学习到的 RC 与化学物理学中的内禀坐标有关，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，或者算法应该对未来多远的时间进行预测。通过对基准系统的仔细比较，我们证明了这个超参数的选择可以有效地控制我们希望对系统的亚稳态分类有多粗粒度。因此，我们认为这项工作代表了在将基于深度学习的思想系统地应用于分子模拟方面迈出了一步。
