Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Gaussian factorization of hydrodynamic correlation functions and mode-coupling memory kernels.

Jianlan Wu1, Jianshu Cao

  • 1Department of Chemistry, Massachusetts Institute of Technology, Cambridge 02139, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 26, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Many-Body Exciton Interactions, Coherence, and Transport in Perovskite Quantum Dots.

Nano letters·2026
Same author

Relation between Structure and Functionality in Photosynthetic Antenna Complex of Green Sulfur Bacteria: Efficiency under Natural Sunlight Pumping.

The journal of physical chemistry. B·2026
Same author

Isolation, Genome Sequencing and Proteomic Analysis of a LiCl-Tolerant Bacillus rugosus E-4 Strain from East Taijinar Salt Lake on the Qinghai-Tibet Plateau.

Current microbiology·2026
Same author

The effect of an optical cavity on diabatic tunneling in an ensemble of symmetric double-well systems.

The Journal of chemical physics·2025
Same author

Association of prior history of suicide attempt and disease severity and subsequent suicidal acts among patients with psychiatric disorders in North China: a multicentre prospective study protocol.

BMJ open·2025
Same author

Enhanced Metal Surface Processing Through the No-Stray-Corrosion Controllable Electrolyte DistributionElectrochemical Machining Method Utilizing a Water-Absorbent Porous Ball.

Micromachines·2025

A new method simplifies calculating memory functions in generalized Langevin equations by linking random forces to liquid interactions. This aids understanding non-Gaussian behavior in supercooled liquids.

Area of Science:

  • Theoretical Chemistry
  • Statistical Mechanics
  • Computational Physics

Background:

  • Generalized Langevin equations (GLEs) are crucial for modeling complex dynamics in liquids.
  • Determining mode-coupling memory functions within GLEs is computationally challenging.
  • Understanding non-Gaussian dynamics is key to characterizing supercooled liquids.

Purpose of the Study:

  • To develop a simplified method for calculating mode-coupling memory functions in GLEs.
  • To derive memory kernels for key correlation functions in molecular liquids.
  • To quantitatively relate non-Gaussian behavior to hydrodynamic mode properties.

Main Methods:

  • Expressing random forces in GLEs using pair interactions.
  • Applying Gaussian factoring to multiple-point time correlation functions.

Related Experiment Videos

  • Deriving mode-coupling memory kernels for velocity, density, and bilinear density correlations.
  • Main Results:

    • A straightforward method to determine mode-coupling memory functions is presented.
    • Explicit memory kernels for linear molecular liquids are derived.
    • Non-Gaussian behavior in bilinear density correlations is quantitatively linked to nonexponential hydrodynamic modes.

    Conclusions:

    • The derived GLEs and memory kernels are valuable for relaxation and spectroscopy in liquids.
    • The established relation provides insights into non-Gaussian indicators in simulations of supercooled liquids.
    • This work offers a simplified approach to complex dynamical calculations in condensed matter.