4.7 Article

Nonlinear measurements of kinetics and generalized dynamical modes. I. Extracting the one-dimensional Green's function from a time series

Journal

JOURNAL OF CHEMICAL PHYSICS
Volume 155, Issue 2, Pages -

Publisher

AIP Publishing
DOI: 10.1063/5.0053422

Keywords

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Funding

  1. National Science Foundation Chemical Measurement and Imaging program [CHE-1707813, CHE-2003619]
  2. Chemical Structures, Dynamics, and Mechanisms - A program
  3. University of South Carolina

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This paper proposes a method of extracting Green's functions through a set of nonlinear correlation functions, primarily for equilibrium time series but also for other types of nonlinear dynamics. The study suggests that obtaining all available information in a time series requires a full set of one-dimensional nonlinear measurements.
Often, a single correlation function is used to measure the kinetics of a complex system. In contrast, a large set of k-vector modes and their correlation functions are commonly defined for motion in free space. This set can be transformed to the van Hove correlation function, which is the Green's function for molecular diffusion. Here, these ideas are generalized to other observables. A set of correlation functions of nonlinear functions of an observable is used to extract the corresponding Green's function. Although this paper focuses on nonlinear correlation functions of an equilibrium time series, the results are directly connected to other types of nonlinear kinetics, including perturbation-response experiments with strong fields. Generalized modes are defined as the orthogonal polynomials associated with the equilibrium distribution. A matrix of mode-correlation functions can be transformed to the complete, single-time-interval (1D) Green's function. Diagonalizing this matrix finds the eigendecays. To understand the advantages and limitation of this approach, Green's functions are calculated for a number of models of complex dynamics within a Gaussian probability distribution. Examples of non-diffusive motion, rate heterogeneity, and range heterogeneity are examined. General arguments are made that a full set of nonlinear 1D measurements is necessary to extract all the information available in a time series. However, when a process is neither dynamically Gaussian nor Markovian, they are not sufficient. In those cases, additional multidimensional measurements are needed.

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