布朗动力学
动力学
流量(数学)
亚稳态
代表(政治)
反作用坐标
还原(数学)
工作(物理)
坐标系
动能
统计物理学
相空间
坐标空间
热力学
生物系统
布朗运动
数学
机械
化学
物理
计算化学
经典力学
几何学
统计
政治
生物
政治学
法学
有机化学
摘要
In this work, we introduce a flow based machine learning approach called reaction coordinate (RC) flow for the 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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