# Reaction coordinate flows for model reduction of molecular kinetics

Source: [Reaction coordinate flows for model reduction of molecular kinetics](<https://www.sota2.com/research/paper/68fa452f1e209131ce916a59>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T05:10:08.292Z

Authors: Hao Wu, Frank No\\'e

Published: 2023-09-11T23:59:18.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2309.05878>)

[PDF](<https://arxiv.org/pdf/2309.05878.pdf>)