Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models.
| Autor: | Girardier DD; Sorbonne Université, Musée National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Materiaux et de Cosmochimie, IMPMC, F-75005 Paris, France., Vroylandt H; Sorbonne Université, Institut des Sciences du Calcul et des données, ISCD, F-75005 Paris, France., Bonella S; Centre Européen de Calcul Atomique et Moléculaire (CECAM), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland., Pietrucci F; Sorbonne Université, Musée National d'Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Materiaux et de Cosmochimie, IMPMC, F-75005 Paris, France. |
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| Jazyk: | angličtina |
| Zdroj: | The Journal of chemical physics [J Chem Phys] 2023 Oct 28; Vol. 159 (16). |
| DOI: | 10.1063/5.0169050 |
| Abstrakt: | Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic rates can in principle be obtained by different techniques after projecting the high-dimensional atomic dynamics on one (or a few) collective variable. Even though it is well-known that the projected dynamics approximately follows - in a statistical sense - the generalized, underdamped or overdamped Langevin equations (depending on the time resolution), to date it is nontrivial to parameterize such equations starting from a limited, practically accessible amount of non-ergodic trajectories. In this work we focus on Markovian, underdamped Langevin equations, that arise naturally when considering, e.g., numerous water-solution processes at sub-picosecond resolution. After contrasting the advantages and pitfalls of different numerical approaches, we present an efficient parametrization strategy based on a limited set of molecular dynamics data, including equilibrium trajectories confined to minima and few hundreds transition path sampling-like trajectories. Employing velocity autocorrelation or memory kernel information for learning the friction and likelihood maximization for learning the free-energy landscape, we demonstrate the possibility to reconstruct accurate barriers and rates both for a benchmark system and for the interaction of carbon nanoparticles in water. (© 2023 Author(s). Published under an exclusive license by AIP Publishing.) |
| Databáze: | MEDLINE |
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