ArticleThe Journal of chemical physics2015
Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism.
Article in The Journal of chemical physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Biomolecular condensates as cellular memory modules: Thermodynamic principles and plant stress adaptation.Biophysical journal · 2026Review
- Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model.Journal of chemical theory and computation · 2024Article
- Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties.JACS Au · 2024Article
- Bottom-Up Informed and Iteratively Optimized Coarse-Grained Non-Markovian Water Models with Accurate Dynamics.Journal of chemical theory and computation · 2023Article
- Bottom-up Coarse-Graining: Principles and Perspectives.Journal of chemical theory and computation · 2022Review
- Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications.Molecules (Basel, Switzerland) · 2022Review
- Likelihood-based non-Markovian models from molecular dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Optimal renormalization of multiscale systems.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- The Middle Science: Traversing Scale In Complex Many-Body Systems.ACS central science · 2021Article
- Fluctuation-dissipation relations far from equilibrium: a case study.Soft matter · 2021Article
- Introducing Memory in Coarse-Grained Molecular Simulations.The journal of physical chemistry. B · 2021Article
- Heat diffusion-related damping process in a highly precise coarse-grained model for nonlinear motion of SWCNT.Scientific reports · 2021Article
- Data-Driven Model Reduction for Stochastic Burgers Equations.Entropy (Basel, Switzerland) · 2020Article
- Mesoscopic Adaptive Resolution Scheme toward Understanding of Interactions between Sickle Cell Fibers.Biophysical journal · 2017Article
- Data-driven parameterization of the generalized Langevin equation.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- 111 years of Brownian motion.Soft matter · 2016Article
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4 authors.
Funding
Abstract
The Mori-Zwanzig formalism for coarse-graining a complex dynamical system typically introduces 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. However, 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. However, 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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