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dc.contributor.authorCalderon, Christopher P.
Arora, Karunesh
dc.date.accessioned 2018-06-19T17:13:02Z
dc.date.available 2018-06-19T17:13:02Z
dc.date.issued 2008-10
dc.identifier.citation Calderon, Christopher P. and Arora, Karunesh. "Extracting Kinetic and Stationary Distribution Information from Short MD Trajectories via a Collection of Surrogate Diffusion Models." (2008) https://hdl.handle.net/1911/102103.
dc.identifier.urihttps://hdl.handle.net/1911/102103
dc.description.abstract Low-dimensional stochastic models can summarize dynamical information and make long time predictions associated with observables of complex atomistic systems. Maximum likelihood based techniques for estimating low-dimensional surrogate diffusion models from relatively short time series are presented. It is found that a heterogeneous population of slowly evolving conformational degrees of freedom modulates the dynamics. This underlying heterogeneity results in a collection of estimated low-dimensional diffusion models. Numerical techniques for exploiting this finding to approximate skewed histograms associated with the simulation are presented. In addition, statistical tests are also used to assess the validity of the models and determine physically relevant sampling information, e.g. the maximum sampling frequency at which one can discretely sample from an atomistic time series and have a surrogate diffusion model pass goodness-of-fit tests. The information extracted from such analyses can possibly be used to assist umbrella sampling computations as well as help in approximating effective diffusion coefficients. The techniques are demonstrated on simulations of Adenylate Kinase.
dc.format.extent 29 pp
dc.title Extracting Kinetic and Stationary Distribution Information from Short MD Trajectories via a Collection of Surrogate Diffusion Models
dc.type Technical report
dc.date.note October 2008
dc.type.dcmi Text


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