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Gaussian mixture transition models for identification of slow processes in molecular kinetics

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The identification of slow processes from molecular dynamics (MD) simulations is a fundamental and important problem for analyzing and understanding complex molecular processes, because the slow processes governed by dominant eigenvalues and eigenfunctions of MD propagators contain essential information on structures and transition rates of metastable conformations. Most of the existing approaches to this problem, including Markov model based approaches and the variational approach, perform the identification by representing the dominant eigenfunctions as linear combinations of a set of basis functions. But the choice of basis functions is still an unsatisfactorily solved problem for these approaches. Here we take a Bayesian approach to slow process identification by developing a novel parametric model called Gaussian mixture transition model (GMTM) to characterize MD propagators. The GMTM approximates the half-weighted density of a MD propagator by a Gaussian mixtur e model and allows for tractable computation of spectral components. In contrast with the other Galerkin-type approximation based approaches, our approach can automatically adjust the involved Gaussian basis functions and handle the statistical uncertainties in the Bayesian framework. We demonstrate by some simulation examples the effectiveness and accuracy of the proposed approach.

This talk is part of the Isaac Newton Institute Seminar Series series.

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