Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

Li, Zhen; Bian, Xin; Li, Xiantao; Karniadakis, George Em

doi:10.1063/1.4935490

Title: Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

Journal Article · Fri Nov 13 00:00:00 EST 2015 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.4935490· OSTI ID:1565466

^[1];

^[1]; Li, Xiantao ^[2]; Karniadakis, George Em ^[1]

Brown Univ., Providence, RI (United States)
Pennsylvania State Univ., University Park, PA (United States)

The Mori-Zwanzig formalism for coarse-graining a complex dynamical system typically presents memory effects. The Markovian assumption of delta-correlated fluctuating forces is often employed to simplify the formulation of coarse-grained (CG) models and numerical implementations. Yet, when the time scales of a system are not clearly separated, the memory effects become strong and the Markovian assumption becomes inaccurate. To this end, we incorporate memory effects into CG modeling by preserving non-Markovian interactions between CG variables, and the memory kernel is evaluated directly from microscopic dynamics. For a specific example, molecular dynamics (MD) simulations of star polymer melts are performed while the corresponding CG system is defined by grouping many bonded atoms into single clusters. Then, the effective interactions between CG clusters as well as the memory kernel are obtained from the MD simulations. The constructed CG force field with a memory kernel leads to a non-Markovian dissipative particle dynamics (NM-DPD). Quantitative comparisons between the CG models with Markovian and non-Markovian approximations indicate that including the memory effects using NM-DPD yields similar results as the Markovian-based DPD if the system has clear time scale separation. In contrast, for systems with small separation of time scales, NM-DPD can reproduce correct short-time properties that are related to how the system responds to high-frequency disturbances, which cannot be captured by the Markovian-based DPD model.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: USDOE Office of Science (SC)

OSTI ID:: 1565466

Alternate ID(s):: OSTI ID: 1225984

Journal Information:: Journal of Chemical Physics, Vol. 143, Issue 24; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 90 works

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