Köhler, Jonas and Chen, Yaoyi and Krämer, Andreas and Clementi, Cecilia and Noé, Frank
(2023)
*Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces.*
J. Chem. Theory Comput., 19
(3).
pp. 942-952.

Full text not available from this repository.

Official URL: https://doi.org/10.1021/acs.jctc.3c00016

## Abstract

Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time and length scales inaccessible to all-atom simulations. Parametrizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we present flow-matching, a new training method for CG force fields that combines the advantages of both methods by leveraging normalizing flows, a generative deep learning method. Flow-matching first trains a normalizing flow to represent the CG probability density, which is equivalent to minimizing the relative entropy without requiring iterative CG simulations. Subsequently, the flow generates samples and forces according to the learned distribution in order to train the desired CG free energy model via force-matching. Even without requiring forces from the all-atom simulations, flow-matching outperforms classical force-matching by an order of magnitude in terms of data efficiency and produces CG models that can capture the folding and unfolding transitions of small proteins.

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: | 2954 |

Deposited By: | Monika Drueck |

Deposited On: | 20 Apr 2023 08:22 |

Last Modified: | 20 Apr 2023 08:22 |

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