A systematic analysis of the memory term in coarse-grained models: The case of the Markovian approximation

Autor: NICODEMO DI PASQUALE, THOMAS HUDSON, MATTEO ICARDI, LORENZO ROVIGATTI, MARCO SPINACI
Rok vydání: 2022
Předmět:
Statistical Mechanics (cond-mat.stat-mech)
Coarse-Grained Models
Applied Mathematics
FOS: Physical sciences
Hardware_PERFORMANCEANDRELIABILITY
Condensed Matter - Soft Condensed Matter
Molecular Dynamics
Memory Effects
molecular dynamics
memory effects
coarse-grained models
Mori-Zwanzig formalism
Markovian approximation
Markovian Approximation
Stochastic Differential and Integral Equations
etc.)
Hardware_INTEGRATEDCIRCUITS
Soft Condensed Matter (cond-mat.soft)
Settore MAT/07 - Fisica Matematica
Condensed Matter - Statistical Mechanics
Asymptotic Approximations and Asymptotic Expansions (Steepest Descent
Molecular Dynamics
Coarse-Grained Models
Mori-Zwanzig formalism
Memory Effects
Markovian Approximation
Stochastic Differential and Integral Equations
Asymptotic Approximations and Asymptotic Expansions (Steepest Descent
etc.)
ISSN: 0956-7925
1469-4425
Popis: The systematic development of Coarse-Grained (CG) models via the Mori-Zwanzig projector operator formalism requires the explicit description of several terms, including a deterministic drift term, a dissipative memory term and a random fluctuation term. In many applications, the memory and fluctuation terms are related by the fluctuation-dissipation relation and are, in general, more challenging to derive than the drift term. In this work we analyse an approximation of the memory term and propose a rational basis for a data-driven approach to an approximation of the memory and fluctuating terms which can be considered included in the class of the Markovian ones.
39 pages, 2 Figures
Databáze: OpenAIRE