The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates.

分子动力学模拟的复杂性需要降维和粗粒度技术以实现易于处理的计算。广义朗之万方程 (GLE) 描述了降维的粗粒度动力学。尽管在非平衡动力学中起着至关重要的作用，但 GLE 的记忆内核经常被忽略，因为它难以表征且求解成本高昂。为了解决这些问题，我们构建了一个数据驱动的 GLE 有理近似。在利用 GLE 模拟简单系统的先前工作的基础上，我们将这些结果扩展到更复杂的分子，这些分子的许多自由度和复杂的动力学需要近似方法。