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 Concept Videos

Atomic Nuclei: Nuclear Spin State Population Distribution01:14

Atomic Nuclei: Nuclear Spin State Population Distribution

1.7K
Near absolute zero temperatures, in the presence of a magnetic field, the majority of nuclei prefer the lower energy spin-up state to the higher energy spin-down state. As temperatures increase, the energy from thermal collisions distributes the spins more equally between the two states. The Boltzmann distribution equation gives the ratio of the number of spins predicted in the spin −½ (N−) and spin +½ (N+) states.
1.7K
Sampling Distribution01:12

Sampling Distribution

17.7K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
17.7K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.4K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.4K
Electrochemical Systems01:24

Electrochemical Systems

180
Electrochemical systems provide a fascinating insight into the dynamic interplay of charged species within various phases. One notable example is the interaction between a membrane permeable to K⁺ ions but not to Cl⁻ ions, separating an aqueous KCl solution from pure water. As K⁺ ions diffuse through the membrane, they generate net charges on each phase, leading to a potential difference between them.Similarly, when a piece of Zn is immersed in an aqueous ZnSO₄ solution,...
180
Equilibrium Conditions for a Particle01:23

Equilibrium Conditions for a Particle

2.5K
When an object is in equilibrium, it is either at rest or moving with a constant velocity. There are two types of equilibrium: static and dynamic. Static equilibrium occurs when an object is at rest, while dynamic equilibrium occurs when an object is moving with a constant velocity. In both cases, there must be a balance of forces acting on the object.
To understand the concept of equilibrium, let us first consider the forces acting on an object. When different forces act on an object, they can...
2.5K
Probability Distributions01:32

Probability Distributions

10.2K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
10.2K

You might also read

Related Articles

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

Sort by
Same author

Gene Expression Profiles at Early vs Late Stages After Cervical Artery Dissection.

Neurology. Genetics·2026
Same author

Influence of Basis Set Composition on Metabolite Quantification of <sup>1</sup>H-MRS at 3 T: Combining In Silico, In Vivo and In Vitro Evidence.

NMR in biomedicine·2026
Same author

Accelerating scRNA-seq Analysis: Automated cell type annotation using representation learning and vector search.

bioRxiv : the preprint server for biology·2025
Same author

Investigating the disturbance in cortical glutamate and GABA function in psychosis and its origins and consequences.

Molecular psychiatry·2025
Same author

Brain and muscle chemistry in myalgic encephalitis/chronic fatigue syndrome (ME/CFS) and long COVID: a 7T magnetic resonance spectroscopy study.

Molecular psychiatry·2025
Same author

Non-invasive brain stimulation reorganises effective connectivity during a working memory task in individuals with Neurofibromatosis Type 1.

Neuroimage. Reports·2025
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Related Experiment Video

Updated: May 6, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

7.6K

Rare-event sampling for driven inertial systems via the nonequilibrium distribution function.

Stephen R Williams1

  • 1Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory 0200, Australia.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 16, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for calculating nonequilibrium rate constants, enhancing molecular simulations across large energy barriers. The technique improves efficiency and handles systems with inertial dynamics, outperforming existing methods like forward flux sampling.

More Related Videos

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles
08:39

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

Published on: October 16, 2017

10.9K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

14.5K

Related Experiment Videos

Last Updated: May 6, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

7.6K
Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles
08:39

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

Published on: October 16, 2017

10.9K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

14.5K

Area of Science:

  • Non-equilibrium statistical mechanics
  • Computational chemistry
  • Molecular dynamics simulations

Background:

  • Generalizations to Yamada-Kawasaki nonlinear response theory enable new importance sampling techniques.
  • Calculating nonequilibrium rate constants across large free energy barriers is computationally challenging.

Purpose of the Study:

  • To develop a new method for calculating nonequilibrium rate constants across large free energy barriers.
  • To extend the Yamada-Kawasaki formalism to stochastic equations of motion.
  • To enhance the efficiency and applicability of molecular simulations for systems far from equilibrium.

Main Methods:

  • Generalization of the Yamada-Kawasaki type nonlinear response theory.
  • Development of a nonequilibrium importance sampling method for deterministic and stochastic dynamics.
  • Quantitative testing on 1D double-well potential models with position-dependent temperature profiles.

Main Results:

  • The developed method accurately calculates nonequilibrium rate constants across significant energy barriers.
  • The method is applicable to systems with inertial equations of motion, unlike traditional stochastic methods.
  • Demonstrated over two orders of magnitude greater efficiency compared to forward flux sampling for the tested models.

Conclusions:

  • The generalized Yamada-Kawasaki formalism provides a powerful tool for nonequilibrium simulations.
  • The new importance sampling method significantly accelerates molecular simulations across large free energy barriers.
  • This approach offers a more efficient and versatile alternative for studying systems far from equilibrium.