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Catalysis

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.

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Related Experiment Video

Updated: Sep 20, 2025

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

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

Published on: April 12, 2019

Computational modelling of nanoparticle catalysis.

Samantha M McIntyre¹, Anna L Garden¹

¹MacDiarmid Institute for Advanced Materials and Nanotechnolgy and Department of Chemistry, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand. anna.garden@otago.ac.nz.

|May 29, 2025

Summary

This summary is machine-generated.

Computational methods offer atomic-level insights into nanocatalysis, revealing unique behaviors of nanoparticles. This review covers computational tools, challenges, and machine learning approaches for understanding catalytic systems.

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Last Updated: Sep 20, 2025

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

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Published on: April 12, 2019

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Area of Science:

Computational chemistry
Materials science
Chemical engineering

Background:

Nanoparticle catalysts are crucial in modern chemistry.
Computational methods provide atomic-level insights into catalytic processes.
Understanding nanocatalysis requires specialized computational approaches.

Purpose of the Study:

To review existing and emerging computational methods in nanocatalysis.
To highlight the unique catalytic behaviors revealed by these methods.
To discuss challenges and future directions in computational nanocatalysis.

Main Methods:

Numerical tools for calculating reaction structures, energies, and rates.
Approaches for representing active sites in nanoparticle catalysts.
Modeling of alloy and supported nanoparticle systems.

Main Results:

Computational methods reveal complex behaviors of alloy and supported nanoparticles.
Challenges include catalyst dynamics, solvent effects, and defect influences.
Machine learning approaches are emerging as powerful tools in nanocatalysis.

Conclusions:

Computational methods are essential for understanding nanocatalysis.
These methods provide unique insights into catalytic mechanisms and properties.
Future work should address dynamic effects, solvent, and defects for realistic modeling.