Reaction coordinate flows for model reduction of molecular kinetics

• URL: http://arxiv.org/abs/2309.05878v1
• Date: Mon, 11 Sep 2023 23:59:18 GMT
• Title: Reaction coordinate flows for model reduction of molecular kinetics
• Authors: Hao Wu and Frank No\'e
• Abstract summary: We introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems. 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.
• Score: 2.6088247674246303
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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