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Iterative Reconstruction of Memory Kernels.

Gerhard Jung1, Martin Hanke1, Friederike Schmid1

  • 1Institut für Physik, ‡Graduate School of Excellence Materials Science in Mainz, §Institut für Mathematik, Johannes Gutenberg-Universität Mainz , Staudingerweg 9, 55128 Mainz, Germany.

Journal of Chemical Theory and Computation
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Summary
This summary is machine-generated.

This study introduces an iterative method for reconstructing memory kernels in coarse-grained models, improving accuracy and enabling larger time steps in simulations of complex systems like Brownian diffusion.

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Area of Science:

  • Computational physics
  • Molecular dynamics
  • Statistical mechanics

Background:

  • Coarse-grained models are increasingly used to simulate systems with slow dynamics.
  • Extracting accurate dynamical properties, like memory kernels, from simulations is challenging.
  • Non-Markovian dynamics are crucial for systems with incomplete time-scale separation.

Purpose of the Study:

  • To develop a systematic method for memory kernel reconstruction in coarse-grained models.
  • To improve the accuracy of coarse-grained dynamics compared to fine-grained simulations.
  • To enable the use of significantly larger time steps in coarse-grained simulations.

Main Methods:

  • Proposed an iterative method for memory kernel reconstruction from dynamical correlation functions.
  • Developed a novel numerical integrator for generalized Langevin equations.
  • Validated methods using backflow-induced memory in Brownian diffusion of a single colloid.

Main Results:

  • The iterative method accurately reproduces target correlation functions, overcoming time step and discretization limitations.
  • The new integrator offers superior accuracy compared to existing methods.
  • Realistic coarse-grained dynamics were achieved with time steps ~200x larger than all-atom simulations.

Conclusions:

  • The developed iterative reconstruction method provides accurate and efficient coarse-grained dynamics.
  • The new numerical integrator enhances the reliability of generalized Langevin equation simulations.
  • These advancements facilitate the study of complex systems with slow dynamics.