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

Measuring Reaction Rates03:09

Measuring Reaction Rates

26.8K
Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical...
26.8K
Multi-Step Reactions02:31

Multi-Step Reactions

8.0K
Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
8.0K
Reaction Rate02:53

Reaction Rate

57.8K
The rate of reaction is the change in the amount of a reactant or product per unit time. Reaction rates are therefore determined by measuring the time dependence of some property that can be related to reactant or product amounts. Rates of reactions that consume or produce gaseous substances, for example, are conveniently determined by measuring changes in volume or pressure.
The mathematical representation of the change in the concentration of reactants and products, over time, is the rate...
57.8K
Concentration and Rate Law03:03

Concentration and Rate Law

34.5K
The rate of a reaction is affected by the concentrations of reactants. Rate laws (differential rate laws) or rate equations are mathematical expressions describing the relationship between the rate of a chemical reaction and the concentration of its reactants.
For example, in a generic reaction aA + bB ⟶ products, where a and b are stoichiometric coefficients, the rate law can be written as:
34.5K
Rate Law and Reaction Order02:33

Rate Law and Reaction Order

10.3K
The rate of a reaction is affected by the concentrations of reactants. Rate laws (differential rate laws) or rate equations are mathematical expressions describing the relationship between the rate of a chemical reaction and the concentration of its reactants.
For example, in a generic reaction aA + bB ⟶ products, where a and b are stoichiometric coefficients, the rate law can be written as:
rate = k[A]m[B]n
[A] and [B] represent the molar concentrations of reactants, and k is the rate...
10.3K
Temperature Dependence on Reaction Rate02:55

Temperature Dependence on Reaction Rate

85.2K
The Collision Theory
Atoms, molecules, or ions must collide before they can react with each other. Atoms must be close together to form chemical bonds. This premise is the basis for a theory that explains many observations regarding chemical kinetics, including factors affecting reaction rates.
The collision theory is based on the postulates that (i) the reaction rate is proportional to the rate of reactant collisions, (ii) the reacting species collide in an orientation allowing contact between...
85.2K

You might also read

Related Articles

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

Sort by
Same author

Percolation in networks with local homeostatic plasticity.

Nature communications·2022
Same author

Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation.

Journal of chemical information and modeling·2021
Same author

Coloured random graphs explain the structure and dynamics of cross-linked polymer networks.

Scientific reports·2020
Same author

Effect of volume growth on the percolation threshold in random directed acyclic graphs with a given degree distribution.

Physical review. E·2020
Same author

Percolation on branching simplicial and cell complexes and its relation to interdependent percolation.

Physical review. E·2020
Same author

Renormalization group for link percolation on planar hyperbolic manifolds.

Physical review. E·2019

Related Experiment Video

Updated: Nov 2, 2025

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

9.2K

Learning heterogenous reaction rates from stochastic simulations.

Ariana Torres-Knoop1, Ivan Kryven2

  • 1SURF, Hoog Catharijne, Moreelsepark 48, 3511 EP Utrecht, the Netherlands.

Physical Review. E
|June 17, 2021
PubMed
Summary

This study introduces a data assimilation method to learn kinetic parameters from molecular dynamics simulations. This allows for more accurate macroscopic chemical kinetics predictions by discovering effective differential equations.

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K
Hot Biological Catalysis: Isothermal Titration Calorimetry to Characterize Enzymatic Reactions
13:00

Hot Biological Catalysis: Isothermal Titration Calorimetry to Characterize Enzymatic Reactions

Published on: April 4, 2014

21.0K

Related Experiment Videos

Last Updated: Nov 2, 2025

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

9.2K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K
Hot Biological Catalysis: Isothermal Titration Calorimetry to Characterize Enzymatic Reactions
13:00

Hot Biological Catalysis: Isothermal Titration Calorimetry to Characterize Enzymatic Reactions

Published on: April 4, 2014

21.0K

Area of Science:

  • Chemical Kinetics
  • Computational Chemistry
  • Data Assimilation

Background:

  • Macroscopic chemical kinetics relies on deterministic reaction rate equations.
  • Microscopic chemical kinetics is stochastic, involving molecular dynamics and collisions.
  • Molecular dynamics can capture complex phenomena affecting reaction rates beyond macroscopic models.

Purpose of the Study:

  • To develop a data assimilation procedure for learning nonhomogeneous kinetic parameters from molecular simulations.
  • To discover effective differential equations for reaction kinetics by upscaling microscopic data.
  • To predict the long-time evolution of macroscopic chemical systems.

Main Methods:

  • Utilizing data assimilation to learn kinetic parameters from molecular simulations.
  • Employing ordinary differential equations to model deterministic chemical kinetics.
  • Simulating molecular dynamics for systems with many simultaneously reacting species.

Main Results:

  • Successfully learned nonhomogeneous kinetic parameters from complex molecular systems.
  • Discovered an effective differential equation for reaction kinetics.
  • Observed peculiar time and temperature dependences in kinetic parameters for a network-forming system.
  • Found a universal distribution for network strand cycle closure probability.

Conclusions:

  • The data assimilation procedure effectively bridges microscopic stochasticity and macroscopic deterministic models.
  • The discovered effective differential equations enhance the predictive power of chemical kinetics models.
  • The study reveals unique kinetic behaviors in complex molecular network formation.