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DY: Fachverband Dynamik und Statistische Physik

DY 41: Modeling and Simulation of Soft Matter II (joint session CPP/DY)

DY 41.4: Vortrag

Donnerstag, 4. April 2019, 10:30–10:45, H13

Automated detection of many-particle solvation states for accurate characterizations of diffusion kinetics — •Joseph Rudzinski, Marc Radu, and Tristan Bereau — Max Planck Institute for Polymer Research, Mainz, Germany

Markov state models are powerful tools for reducing the complexity of molecular dynamics trajectories, but require configuration-space representations that accurately characterize the relevant dynamics. Well-established, low-dimensional order parameters for constructing this representation have led to widespread applications to study conformational dynamics of biomolecules. On the contrary, applications to characterize single-molecule diffusion have been scarce and typically employ system-specific, higher-dimensional order parameters to characterize local solvation states. In this work, we propose an automated method for generating a coarse configuration-space representation, using the coordination numbers about each particle. To overcome the noisy behavior of these low-dimensional observables, we treat the features as indicators of a latent Markov process. The resulting hidden Markov models filter the trajectories of each feature into the most likely latent solvation state at each time step. The filtered trajectories are then used to construct a configuration-space discretization, which accurately describes the diffusion kinetics. The method is validated on a standard model for glassy liquids, where particle jumps between local cages determine the diffusion properties. The resulting models provide quantitatively accurate characterizations of the diffusion constant and also reveal a mechanistic description of diffusive jumps.