分子动力学模型降维的反应坐标流
Reaction coordinate flows for model reduction of molecular kinetics
AI总结：
提出基于流的机器学习方法RC流，利用归一化流和布朗动力学模型发现分子系统低维动力学模型，有效提供连续时空下可训练的降维模型并识别亚稳态。
摘要：
在这项工作中，我们引入了一种基于流的机器学习方法，称为反应坐标（RC）流，用于发现分子系统的低维动力学模型。RC流利用归一化流来设计坐标变换，并利用布朗动力学模型来近似RC的动力学，其中所有模型参数都可以以数据驱动的方式进行估计。与现有的分子动力学模型降维方法相比，由于归一化流的可逆性，RC流提供了一个可训练且易于处理的连续时间和空间的降维动力学模型。此外，本工作中研究的基于布朗动力学的降维动力学模型，在分子系统的相空间中产生了易于识别的亚稳态表示。数值实验证明了该方法如何有效地从模拟中发现给定全态动力学的可解释且准确的低维表示。
英文摘要：
In this work, we introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems. The RC flow utilizes a normalizing flow to design the coordinate transformation and a Brownian dynamics model to approximate the kinetics of RC, where all model parameters can be estimated in a data-driven manner. In contrast to existing model reduction methods for molecular kinetics, RC flow offers a trainable and tractable model of reduced kinetics in continuous time and space due to the invertibility of the normalizing flow. Furthermore, the Brownian dynamics-based reduced kinetic model investigated in this work yields a readily discernible representation of metastable states within the phase space of the molecular system. Numerical experiments demonstrate how effectively the proposed method discovers interpretable and accurate low-dimensional representations of given full-state kinetics from simulations.
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