Data-driven Langevin modeling of nonequilibrium processes
arXiv:2105.04344 · doi:10.1021/acs.jpcb.1c03828
Abstract
Given nonstationary data from molecular dynamics simulations, a Markovian Langevin model is constructed that aims to reproduce the time evolution of the underlying process. While at equilibrium the free energy landscape is sampled, nonequilibrium processes can be associated with a biased energy landscape, which accounts for finite sampling effects and external driving. Extending the data-driven Langevin equation (dLE) approach [Phys.\ Rev.\ Lett.\ {\bf 115}, 050602 (2015)] to the modeling of nonequilibrium processes, an efficient way to calculate multidimensional Langevin fields is outlined. The dLE is shown to correctly account for various nonequilibrium processes, including the enforced dissociation of sodium chloride in water, the pressure-jump induced nucleation of a liquid of hard spheres, and the conformational dynamics of a helical peptide sampled from nonstationary short trajectories.
J. Phys. Chem. B 2021. This preprint is the unformatted and early version of a manuscript that has been published in an updated version in the Journal of Physical Chemistry B and may be downloaded for private use only. Copyright will with the journal / ACS
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