Likelihood-based non-Markovian models from molecular dynamics
In: ISSN: 0027-8424, 2022
Online
academicJournal
Zugriff:
International audience ; We introduce a new method to accurately and eciently estimate the eective dynamics of collective variables in molecular simulations. Such reduced dynamics play an essential role in the study of a broad class of processes, ranging from chemical reactions in solution to conformational changes in biomolecules or phase transitions in condensed matter systems. The standard Markovian approximation often breaks down due to the lack of a proper separation of time scales and memory eects must be taken into account. Using a parametrization based on hidden auxiliary variables, we obtain a generalized Langevin equation by maximizing the statistical likelihood of the observed trajectories. Both the memory kernel and random noise are correctly recovered by this procedure. This data-driven approach provides a reduced dynamical model for multidimensional collective variables, enabling the accurate sampling of their long-time dynamical properties at a computational cost drastically reduced with respect to all-atom numerical simulations. The present strategy, based on the reproduction of the dynamics of trajectories rather than the memory kernel or the velocityautocorrelation function, conveniently provides other observables beyond these two, including e.g. stationary currents in non-equilibrium situations, or the distribution of rst passage times between metastable states.
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Likelihood-based non-Markovian models from molecular dynamics
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Autor/in / Beteiligte Person: | Vroylandt, Hadrien ; Goudenège, Ludovic ; Monmarché, Pierre ; Pietrucci, Fabio ; Rotenberg, Benjamin ; Institut des Sciences du Calcul et des Données (ISCD) ; Sorbonne Université (SU) ; Fédération de Mathématiques de CentraleSupélec (FR3487) ; CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) ; Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)) ; Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) ; Laboratoire de chimie théorique (LCT) ; Institut de Chimie - CNRS Chimie (INC-CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) ; Physique des systèmes simples en conditions extrêmes IMPMC (IMPMC_PHYSIX) ; Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC) ; Muséum national d'Histoire naturelle (MNHN)-Institut de recherche pour le développement IRD : UR206-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de recherche pour le développement IRD : UR206-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) ; PHysicochimie des Electrolytes et Nanosystèmes InterfaciauX (PHENIX) |
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Zeitschrift: | ISSN: 0027-8424, 2022 |
Veröffentlichung: | HAL CCSD ; National Academy of Sciences, 2022 |
Medientyp: | academicJournal |
DOI: | 10.1073/pnas.2117586119 |
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