Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Generalized Langevin Equations

Description

This lecture covers the concept of Generalized Langevin Equations, focusing on non-Markovian formulations and their computational implications in molecular dynamics simulations. The instructor discusses the mapping of non-Markovian dynamics onto Markovian dynamics in an extended phase space, highlighting the significant impact of noise details on particle trajectories.

Official source

Related concepts (26)

Molecular dynamics

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanical force fields.

Molecular modelling

Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies. The simplest calculations can be performed by hand, but inevitably computers are required to perform molecular modelling of any reasonably sized system.

Computational chemistry

Computational chemistry is a branch of chemistry that uses computer simulation to assist in solving chemical problems. It uses methods of theoretical chemistry, incorporated into computer programs, to calculate the structures and properties of molecules, groups of molecules, and solids. It is essential because, apart from relatively recent results concerning the hydrogen molecular ion (dihydrogen cation, see references therein for more details), the quantum many-body problem cannot be solved analytically, much less in closed form.

Coarse-grained modeling

Coarse-grained modeling, coarse-grained models, aim at simulating the behaviour of complex systems using their coarse-grained (simplified) representation. Coarse-grained models are widely used for molecular modeling of biomolecules at various granularity levels. A wide range of coarse-grained models have been proposed. They are usually dedicated to computational modeling of specific molecules: proteins, nucleic acids, lipid membranes, carbohydrates or water.

Open quantum system

In physics, an open quantum system is a quantum-mechanical system that interacts with an external quantum system, which is known as the environment or a bath. In general, these interactions significantly change the dynamics of the system and result in quantum dissipation, such that the information contained in the system is lost to its environment. Because no quantum system is completely isolated from its surroundings, it is important to develop a theoretical framework for treating these interactions in order to obtain an accurate understanding of quantum systems.

Related lectures (31)

Uncertainty Quantification for Coarse-Grained Molecular Dynamics

Delves into uncertainty quantification in molecular dynamics, focusing on aerospace materials, modeling decisions, and coarse-grained dynamics.

Recovering dynamical properties: Path Integral Methods

Explores deconvolution of GLE spectra to predict and extract dynamical properties from MD.

Hierarchical Multi-scale Modeling of Molecular Systems

Explores the trends and challenges in modeling complex molecular systems using hierarchical multi-scale approaches, covering length-time scales, atomistic simulations, and force matching techniques.

Equilibrium GLE sampling

Explores optimizing Equilibrium GLE sampling efficiency and custom-tailored thermostats in atomistic modeling.

Effective Dynamics for Stochastic Differential Equations