Geodesic
Generative methods for sampling transition paths in molecular dynamics
Tony Lelièvre1 ; Geneviève Robin2 ; Innas Sekkat3 ; Gabriel Stoltz1 ; Gabriel Victorino Cardoso4
1 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 CMAP, Ecole Polytechnique, Palaiseau, France & LIRYC, Université de Bordeaux, Bordeaux, France
ESAIM. Proceedings, Tome 73 (2023), pp. 238-256 Cet article a éte moissonné depuis la source EDP Sciences

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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.
DOI : 10.1051/proc/202373238

Tony Lelièvre 1 ; Geneviève Robin 2 ; Innas Sekkat 3 ; Gabriel Stoltz 1 ; Gabriel Victorino Cardoso 4

1 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 CMAP, Ecole Polytechnique, Palaiseau, France & LIRYC, Université de Bordeaux, Bordeaux, France 
@article{EP_2023_73_a12,
     author = {Tony Leli\`evre and Genevi\`eve Robin and Innas Sekkat and Gabriel Stoltz and Gabriel Victorino Cardoso},
     title = {Generative methods for sampling transition paths in molecular dynamics},
     journal = {ESAIM. Proceedings},
     pages = {238--256},
     year = {2023},
     volume = {73},
     doi = {10.1051/proc/202373238},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/proc/202373238/}
}
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%A Innas Sekkat
%A Gabriel Stoltz
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%J ESAIM. Proceedings
%D 2023
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Tony Lelièvre; Geneviève Robin; Innas Sekkat; Gabriel Stoltz; Gabriel Victorino Cardoso. Generative methods for sampling transition paths in molecular dynamics. ESAIM. Proceedings, Tome 73 (2023), pp. 238-256. doi: 10.1051/proc/202373238

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