Volume 73, 2023CEMRACS 2021 - Data Assimilation and Reduced Modeling for High Dimensional Problems
|Page(s)||238 - 256|
|Published online||30 August 2023|
Generative methods for sampling transition paths in molecular dynamics
CERMICS, Ecole des Ponts, Marne-la-Vallée, France & MATHERIALS team-project, Inria Paris, France
2 CNRS & Université d’Evry, France & CMAP, Ecole Polytechnique, Palaiseau, France
3 CERMICS, Ecole des Ponts, Marne-la-Vallée, France
4 CERMICS, Ecole des Ponts, Marne-la-Vallée, France & MATHERIALS team-project, Inria Paris, France
5 CMAP, Ecole Polytechnique, Palaiseau, France & LIRYC, Université de Bordeaux, Bordeaux, France
Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one – a behavior known as metastability. Simulating transition paths linking one metastable state to another one is difficult by direct numerical methods. In view of the promises of machine learning techniques, we explore in this work two approaches to more efficiently generate transition paths: sampling methods based on generative models such as variational autoencoders, and importance sampling methods based on reinforcement learning.
© EDP Sciences, SMAI 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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