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

Heterogeneous Catalysis01:22

Heterogeneous Catalysis

Heterogeneous catalysis involves a catalyst in a different phase from the reactants. It is a process where the catalyst and the reactants are in distinct phases, typically solid and gas or liquid.Most heterogeneous catalysts are metals, metal oxides, or acids. The list includes transition metals like iron (Fe), cobalt (Co), nickel (Ni), palladium (Pd), platinum (Pt), chromium (Cr), manganese (Mn), tungsten (W), silver (Ag), and copper (Cu). These metals possess partially vacant d orbitals that...
Catalysis01:27

Catalysis

Catalysis influences the rate of chemical reactions by providing an alternative reaction pathway with lower activation energy. A catalyst speeds up a reaction, but it is not consumed during the process. The fundamental principle of catalysis is the ability of a catalyst to alter the reaction mechanism, often introducing a more efficient pathway than the uncatalyzed process.In a catalyzed reaction, the catalyst participates directly in the reaction mechanism. It interacts with reactants to form...
Catalysis02:50

Catalysis

The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.
Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

Catalytic hydrogenation of alkenes is a transition-metal catalyzed reduction of the double bond using molecular hydrogen to give alkanes. The mode of hydrogen addition follows syn stereochemistry.
The metal catalyst used can be either heterogeneous or homogeneous. When hydrogenation of an alkene generates a chiral center, a pair of enantiomeric products is expected to form. However, an enantiomeric excess of one of the products can be facilitated using an enantioselective reaction or an...
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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.
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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 a mild...

You might also read

Related Articles

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

Sort by
Same author

Development of a gene signature related to phospholipid metabolism for prognosis and therapeutic Prediction in Osteosarcoma: Focus on VAC14.

Gene·2026
Same author

Impact of incorporating Chinese cultural elements into product attributes on purchase intentions via multiple mediating roles of cultural identity: Evidence from the Forbidden City.

Acta psychologica·2026
Same author

Tumor organoids as a revolutionary platform for advancing cancer nanomedicine.

Molecular cancer·2026
Same author

Natural bioactive sericin-based hydrogel dressing for visual pH monitoring and infected wound therapy.

International journal of biological macromolecules·2026
Same author

Embedded Shared EHR in Web Referrals: Early Outcomes from the Yonago City Healthcare Platform.

Studies in health technology and informatics·2026
Same author

The actual Co(10-12) surface structure and CO activation.

Nature communications·2026
Same journal

DeepDOX1: A Dual-Drive Framework Integrating Deep Learning and First-Principles Quantum Chemistry for Drug-Protein Affinity Prediction.

JACS Au·2026
Same journal

Catalyst-Controlled Regiodivergent C-H Olefination of Furanyl Carbamates through a Rational Approach.

JACS Au·2026
Same journal

Charting the Biosynthetic Landscape of Hybrid Polyketide-Nonribosomal Peptide-Specialized Lipids.

JACS Au·2026
Same journal

Valence-State-Dependent Surface Lattice Oxygen in CeO<sub>2</sub>‑Modified VPO Catalysts: Elucidating the Mechanism of <i>n</i>‑Butane Selective Oxidation to Maleic Anhydride.

JACS Au·2026
Same journal

Quantitative Insights into Pressure-Dependent Mass Transport and Reaction Kinetics in Electrochemical CO<sub>2</sub> Reduction.

JACS Au·2026
Same journal

3‑Methylthiopropionic Acid Kills Carbapenem-Resistant <i>Klebsiella pneumoniae</i> by Disrupting Membrane Integrity and Bioenergetics.

JACS Au·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2026

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

Smarter Data: Rethinking Data Generation for Machine Learning Potentials in Heterogeneous Catalysis.

Wenbo Xie1, Yixiao Han1, Chenyu Wu1

  • 1School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China.

JACS Au
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Generating the right data is crucial for reliable machine-learning potentials (MLPs) in heterogeneous catalysis. This work reinterprets data generation strategies, focusing on scope, relevance, and coverage for improved simulations.

Keywords:
heterogeneous catalysisinteraction-based samplingmachine-learning potentials (MLPs)structure-based samplingtraining data generation

More Related Videos

Preparation and 3D Tracking of Catalytic Swimming Devices
06:50

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production
08:40

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Published on: December 6, 2021

Related Experiment Videos

Last Updated: Jun 27, 2026

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

Preparation and 3D Tracking of Catalytic Swimming Devices
06:50

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production
08:40

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Published on: December 6, 2021

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Engineering

Background:

  • Machine-learning potentials (MLPs) offer high accuracy for simulating catalytic systems.
  • The reliability of MLPs is critically dependent on the quality and representativeness of training data.
  • Heterogeneous catalysis involves complex, dynamic systems requiring diverse structural and chemical information.

Purpose of the Study:

  • To re-evaluate strategies for constructing training datasets for MLPs in heterogeneous catalysis.
  • To address the challenge of generating 'right' data, not just 'more' data.
  • To propose a hybrid data generation approach for enhanced MLP accuracy.

Main Methods:

  • Analysis of existing MLP training set construction strategies (scope, relevance, coverage).
  • Examination of structure-based and interaction-based sampling methods.
  • Conceptualization of a hybrid data generation workflow.

Main Results:

  • Existing structure-based sampling methods prioritize system relevance and targeted coverage.
  • Emerging interaction-based strategies expand local interaction support beyond predefined systems.
  • A hybrid approach combining structure-based and interaction-based sampling is proposed.

Conclusions:

  • The focus must shift from data quantity to data quality for reliable MLPs.
  • Hybrid data generation workflows can leverage the strengths of different sampling strategies.
  • This approach advances the simulation accuracy of complex heterogeneous catalytic systems using MLPs.