(390a) Statistical Inference of Transport Mechanisms from Time Series of Solute Trajectories in Polymer Membranes

Time series modeling of complex diffusion in soft matter systems on the microsecond time scale can provide a path toward inferring transport mechanisms and predicting bulk properties characteristic of much longer time scales. In this presentation, I discuss the application of nonparametric Bayesian time series analysis, more specifically the sticky hierarchical Dirichlet process autoregressive hidden Markov model (HDP-AR-HMM) to solute center-of-mass trajectories generated from long molecular dynamics (MD) simulations to automatically detect a variety of solute dynamical modes. Along with parameters of individual states, the HDP-AR-HMM simultaneously infers a transition matrix which allows us to stochastically propagate solute behavior from all of the independent trajectories onto arbitrary length time scales while still preserving the qualitative behavior characteristic of the MD trajectories. This affords a direct connection to important macroscopic observables used to characterize performance like solute flux and selectivity. We can use our detailed mechanistic understanding to construct a range of stochastic models rooted in anomalous diffusion theory, which we can use to project out the long term behavior of the solutes and ultimately predict selectivity.

I discuss the use of this technique in nanostructured membranes including both hexagonal and bicontinuous cubic membranes, as well as in amorphous sulfonamide membranes. This work provides a promising way to simultaneously identify transport mechanisms in nanoporous materials and project complex diffusive behavior on long time scales.