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Heterogeneous Catalysis

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...

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Related Experiment Video

Updated: Jul 2, 2026

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 protocol for modeling supported nanoparticle catalysts with strong metal-support interactions.

Gerardo Valadez Huerta¹, Michihisa Koyama¹

¹Institute for Aqua Regeneration, Shinshu University, Nagano, Nagano 380-8553, Japan.

|June 30, 2026

Summary

This study introduces a Python-based computational protocol for modeling complex nanoparticle catalysts. The framework helps identify key catalytic configurations by matching simulated and experimental infrared spectra.

Keywords:

Chemistry Computer sciences Material sciences

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Last Updated: Jul 2, 2026

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Published on: April 12, 2019

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Area of Science:

Computational chemistry
Materials science
Catalysis

Background:

Modeling supported nanoparticle catalysts with strong metal-support interactions (SMSI) is complex.
Existing methods struggle with the high structural complexity of these systems.

Purpose of the Study:

To present a computational protocol for modeling SMSI nanoparticle catalysts.
To provide a modular and adaptive Python-based framework for this purpose.

Main Methods:

Developing a Python framework for constructing supported nanoparticle models.
Exploring large configurational spaces of adsorption states.
Implementing analysis workflows to match simulated and experimental infrared (IR) spectra.

Main Results:

The protocol enables efficient construction of nanoparticle models.
It facilitates exploration of diverse adsorption configurations.
Matching simulated and experimental IR spectra identifies catalytically relevant states.

Conclusions:

The presented computational protocol simplifies modeling of SMSI nanoparticle catalysts.
This framework aids in identifying critical catalytic configurations.
The approach integrates simulation and experimental data for enhanced understanding.