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Updated: Aug 15, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
Published on: December 4, 2017
Formal derivation of dissipative particle dynamics from first principles
David Cubero1, Sophia N Yaliraki
1Department of Chemistry, South Kensington Campus, Imperial College London, London SW7 2AZ, United Kingdom.
The Markovian approximation in particle-based coarse-grained methods is unreliable when sound is important. This study reveals long-lived memory kernels, questioning the link between these methods and molecular dynamics.
Area of Science:
- Computational physics
- Soft matter physics
- Chemical physics
Background:
- Particle-based coarse-grained (CG) techniques are widely used in computational physics and chemistry.
- Dissipative particle dynamics (DPD) is a popular CG method that assumes a Markovian approximation.
- The reliability of the Markovian approximation in CG methods is crucial for accurate simulations.
Purpose of the Study:
- To investigate the validity of the Markovian approximation in CG techniques.
- To analyze the role of sound propagation in CG simulations.
- To assess the connection between CG methods and molecular dynamics (MD).
Main Methods:
- Analytical and numerical solutions for coarse-grained harmonic systems.
- First-principle methods for simulating CG dynamics.
- Investigation of memory kernel effects in CG models.
Main Results:
- The Markovian approximation is shown to be unreliable in systems where sound propagation is significant.
- Long-lived memory kernels were identified in coarse-grained harmonic systems.
- The presence of memory effects challenges the direct connection to molecular dynamics.
Conclusions:
- Current particle-based coarse-grained techniques may not accurately capture dynamics involving sound.
- The identified memory kernels suggest limitations in the Markovian assumption for certain systems.
- Revisiting the theoretical foundations of CG methods is necessary for improved accuracy and connection to MD.
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