Wu, Hao and Noé, Frank
(2024)
*Reaction coordinate flows for model reduction of molecular kinetics.*
The Journal of Chemical Physics, 160
(4).

Full text not available from this repository.

Official URL: https://doi.org/10.1063/5.0176078

## Abstract

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.

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences Mathematical and Computer Sciences > Mathematics Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics |

ID Code: | 3167 |

Deposited By: | Lukas-Maximilian Jaeger |

Deposited On: | 23 Aug 2024 11:00 |

Last Modified: | 23 Aug 2024 11:00 |

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