Data-driven Langevin modeling of nonequilibrium processes

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Nowadays all-atom molecular dynamics simulations produce huge amounts of data, whose systematic analysis poses a major challenge. To facilitate a simple interpretation of the underlying physics, we have recently developed a data-driven Langevin equation approach thatconstructs a low-dimensional dynamical model from a given molecular dynamics trajectory.Within the Research Unit ”Reducing complexity of nonequilibrium systems,” this project extends the data-driven Langevin equationapproach to nonequilibrium processes, where we either start in anonstationary state and study the relaxation of the system into itsequilibrium state, or consider an externally driven system. To thisend, we construct nonequilibrium Langevin models for a variety ofcurrently studied problems, including ligand unbinding andligand-induced allosteric transitions in various proteinsystems. Aiming for a Markovian description, we explore variousmeasures to optimize the Markovianity of the Langevin fields.