论文标题
在机器学习的帮助下表征亚稳态状态
Characterizing metastable states with the help of machine learning
论文作者
论文摘要
当今的原子模拟产生了越来越复杂的系统的长轨迹。分析这些数据，发现亚稳态状态并发现其性质变得越来越具有挑战性。在本文中，我们首先使用变分方法来构象动力学来发现模拟的最慢动力学模式。这允许系统的不同亚稳态态在层次上定位和组织。通过机器学习方法发现了表征亚稳态状态的物理描述符。我们在两种蛋白质蛋白（Chignolin和牛胰腺胰蛋白酶抑制剂）的情况下显示，如何在几秒钟内毫不费力地进行此类分析。我们方法的另一个优势是，它可以应用于对无偏见和有偏见的分析。
Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature is becoming increasingly challenging. In this paper, we first use the variational approach to conformation dynamics to discover the slowest dynamical modes of the simulations. This allows the different metastable states of the system to be located and organized hierarchically. The physical descriptors that characterize metastable states are discovered by means of a machine learning method. We show in the cases of two proteins, Chignolin and Bovine Pancreatic Trypsin Inhibitor, how such analysis can be effortlessly performed in a matter of seconds. Another strength of our approach is that it can be applied to the analysis of both unbiased and biased simulations.
