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.

翻译：本文提出了一种基于流的机器学习方法——反应坐标流，用于发现分子系统的低维动力学模型。该反应坐标流利用归一化流设计坐标变换，并采用布朗动力学模型近似反应坐标的动力学行为，其中所有模型参数均可通过数据驱动方式估计。相较于现有分子动力学模型降阶方法，由于归一化流的可逆性，反应坐标流能够提供连续时间与空间中可训练且可求解的降阶动力学模型。此外，本研究提出的基于布朗动力学的降阶动力学模型，可清晰辨识分子系统相空间中的亚稳态结构。数值实验表明，该方法能有效从模拟数据中发掘可解释且高精度的低维全状态动力学表征。