Data-driven Langevin modeling of nonequilibrium processes

目前，全原子分子动力学模拟产生了大量的数据，对这些数据的系统分析是一个重大挑战。为了方便对潜在物理的简单解释，我们最近开发了一种数据驱动的朗格万方程方法，该方法从给定的分子动力学轨迹构建了一个低维动力学模型。在“降低非平衡系统的复杂性”研究单元中，该项目将数据驱动的朗格万方程方法扩展到非平衡过程，我们要么从非平稳状态开始，研究系统进入平衡状态的松弛，要么考虑外部驱动的系统。为此，我们为目前研究的各种问题构建了非平衡朗格万模型，包括各种蛋白质系统中的配体解结合和配体诱导的变构转变。以马尔可夫描述为目标，探讨了优化朗格万油田马尔可夫性的各种措施。

Nowadays all-atom molecular dynamics simulations produce huge amounts of data, whose systematic analysis poses a major challenge. To facilitate a simple interpretation of the underlying physics, we have recently developed a data-driven Langevin equation approach thatconstructs a low-dimensional dynamical model from a given molecular dynamics trajectory.Within the Research Unit ”Reducing complexity of nonequilibrium systems,” this project extends the data-driven Langevin equationapproach to nonequilibrium processes, where we either start in anonstationary state and study the relaxation of the system into itsequilibrium state, or consider an externally driven system. To thisend, we construct nonequilibrium Langevin models for a variety ofcurrently studied problems, including ligand unbinding andligand-induced allosteric transitions in various proteinsystems. Aiming for a Markovian description, we explore variousmeasures to optimize the Markovianity of the Langevin fields.