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This study introduces a new method for creating accurate coarse-grained (CG) models that preserve non-equilibrium dynamics. The approach uses the non-stationary generalized Langevin equation (nsGLE) for efficient and generalizable modeling of complex systems.

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Area of Science:

  • Computational physics
  • Soft matter physics
  • Chemical physics

Background:

  • Coarse-grained (CG) modeling reduces computational cost for high-dimensional systems.
  • Traditional CG models often alter system dynamics due to lost degrees of freedom.
  • Existing CG models primarily focus on equilibrium systems, limiting their application to non-equilibrium processes.

Purpose of the Study:

  • To develop accurate and efficient CG models that preserve non-equilibrium dynamics.
  • To create a generally applicable method for any non-equilibrium process and observable.
  • To enable bottom-up prediction of main system features for soft matter and other complex systems.

Main Methods:

  • Developed a dynamic equation for CG variables using the non-stationary generalized Langevin equation (nsGLE).
  • Determined the two-time memory kernel from the auto-correlation function of the observable.
  • Embedded the nsGLE in an extended dynamics framework for efficient solution and prediction.
  • Parameterized the memory kernel using a two-time exponential expansion.
  • Proposed a data-driven hybrid optimization integrating differential-evolution and Levenberg-Marquardt algorithms.

Main Results:

  • Successfully established a framework for CG modeling that preserves non-equilibrium dynamics.
  • Demonstrated efficient prediction of non-equilibrium dynamics for CG variables.
  • The proposed hybrid optimization efficiently solved the complex parameterization problem.
  • The method is generally applicable to various non-equilibrium systems and observables.

Conclusions:

  • The nsGLE-based approach provides an accurate and efficient method for CG modeling of non-equilibrium dynamics.
  • This work extends CG modeling capabilities to systems out of equilibrium, crucial for soft matter applications.
  • The developed data-driven optimization ensures robust parameterization for complex systems.