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

Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

8.2K
For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
8.2K
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

4.0K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
4.0K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.4K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.4K
Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

21.3K
The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
21.3K
Enzymes02:34

Enzymes

81.6K
Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...
81.6K
Enzymes and Activation Energy01:13

Enzymes and Activation Energy

12.0K
The activation energy (or free energy of activation), abbreviated as Ea, is the small amount of energy input necessary for all chemical reactions to occur. During chemical reactions, certain chemical bonds break, and new ones form. For example, when a glucose molecule breaks down, bonds between the molecule's carbon atoms break. Since these are energy-storing bonds, they release energy when broken. However, the molecule must be somewhat contorted to get into a state that allows the bonds to...
12.0K

You might also read

Related Articles

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

Sort by
Same author

Impact of Intrinsic Defects and Tungsten Doping on the Catalytic Properties of Two-Dimensional Cu<sub>2</sub>S.

ACS omega·2026
Same author

Spleen Volume Reduction and Transfusion Independence With Momelotinib Versus Ruxolitinib and Associated Overall Survival With Momelotinib in JAK Inhibitor-Naive Patients With Myelofibrosis and Anemia: Subgroup Analyses of SIMPLIFY-1.

Clinical lymphoma, myeloma & leukemia·2026
Same author

YTaNO<sub>2</sub> Janus MXene as an optimal electrocatalyst for the hydrogen evolution reaction.

Physical chemistry chemical physics : PCCP·2026
Same author

Metal-free visible-light carbonylation of alkyl iodides to amides <i>via</i> consecutive photoinduced electron transfer.

Chemical science·2026
Same author

Improving the Runtime of Quantum Phase Estimation for Chemistry through Basis Set Optimization.

Journal of chemical theory and computation·2025
Same author

Energetics and kinetics of alkali ion exchange in analcime.

Physical chemistry chemical physics : PCCP·2025

Related Experiment Video

Updated: Jul 9, 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

9.0K

Reference-Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory.

Jérôme Rey1, Céline Chizallet2, Dario Rocca1

  • 1Laboratoire de Physique et Chimie Théoriques LPCT UMR 7019-CNRS, Université de Lorraine, Vandœuvre-lés-Nancy, France.

Angewandte Chemie (International Ed. in English)
|December 6, 2023
PubMed
Summary

High-level electronic structure calculations are essential for accurately predicting alkene cracking and isomerization reaction energies. This study uses Random Phase Approximation (RPA) and machine learning to achieve unprecedented agreement with experimental data.

Keywords:
ab initio molecular dynamicscatalysishydrocrackingmachine learningzeolites

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

12.8K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.3K

Related Experiment Videos

Last Updated: Jul 9, 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

9.0K
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

12.8K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.3K

Area of Science:

  • Computational Chemistry
  • Chemical Kinetics
  • Materials Science

Background:

  • Alkene transformations catalyzed by zeolites are crucial for converting feedstocks like plastic waste and crude oil into valuable chemicals.
  • Accurate prediction of reaction free energies is vital for understanding and optimizing these catalytic processes.
  • Previous computational methods have struggled to reconcile theoretical predictions with experimental observations.

Purpose of the Study:

  • To accurately calculate the free energies of activation for alkene cracking and isomerization reactions.
  • To evaluate the necessity of high-level electronic structure methods for bridging the gap between theory and experiment.
  • To improve the predictive power of computational chemistry for zeolite-catalyzed hydrocarbon transformations.

Main Methods:

  • Combining multiple electronic structure methods with molecular dynamics simulations.
  • Utilizing the Random Phase Approximation (RPA) level of theory for high accuracy.
  • Employing Machine Learning thermodynamic Perturbation Theory (MLPT) for free energy calculations.
  • Comparing results with PBE+D2 production level calculations and experimental data.

Main Results:

  • The Random Phase Approximation (RPA) level of theory is necessary for accurate free energy calculations, significantly improving upon lower-level methods.
  • Machine Learning thermodynamic Perturbation Theory (MLPT) combined with RPA yielded a significant decrease in isomerization barriers and a similar increase in cracking barriers.
  • The computed free energy barriers showed unprecedented agreement with experimental and kinetic modeling results.
  • Constrained ab initio molecular dynamics at the PBE+D2 level underestimated the experimental barriers.

Conclusions:

  • High-level electronic structure calculations, specifically RPA, are indispensable for accurately modeling zeolite-catalyzed alkene reactions.
  • The combination of RPA and MLPT offers a powerful approach for predicting reaction energetics with high fidelity.
  • This work provides a robust computational framework for the rational design and optimization of catalytic processes for chemical valorization.