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Updated: Jul 9, 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

Automated MUltiscale simulation environment.

Albert Sabadell-Rendón¹, Kamila Kaźmierczak², Santiago Morandi^1,3

¹Institute of Chemical Research of Catalonia (ICIQ-CERCA), The Barcelona Institute of Science and Technology, (BIST) Av. Paisos Catalans 16 Tarragona 43007 Spain asabadell@iciq.es nlopez@iciq.es.

Digital Discovery

|December 6, 2023

Summary

This summary is machine-generated.

We developed Automated MUltiscale Simulation Environment (AMUSE) to bridge atomistic simulations and reactor-scale predictions for heterogeneous catalysis. AMUSE streamlines complex modeling, aiding catalyst design from material to reactor levels.

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Last Updated: Jul 9, 2025

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

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Area of Science:

Chemical Engineering
Materials Science
Computational Chemistry

Background:

Multiscale modeling of heterogeneous catalytic reactors is crucial but faces implementation challenges.
Key challenges include catalytic complexity and disparate time/length scales in phenomena.
Existing methods lack seamless integration from atomistic data to reactor performance prediction.

Purpose of the Study:

To introduce the Automated MUltiscale Simulation Environment (AMUSE) for seamless multiscale modeling of heterogeneous catalytic reactors.
To automate the workflow from Density Functional Theory (DFT) data to microkinetic modeling and Computational Fluid Dynamics (CFD) integration.
To provide a comprehensive tool for catalyst design from atomistic to reactor scales.

Main Methods:

AMUSE workflow starts with Density Functional Theory (DFT) data.
Automated reaction network analysis using graph theory.
Integration of microkinetic models into open-source Computational Fluid Dynamics (CFD) code.

Main Results:

Demonstrated AMUSE on iso-propanol dehydrogenation and CO2 hydrogenation over Pd/In2O3 catalyst.
Successfully integrated atomistic insights with reactor-scale simulations.
Validated the capability of AMUSE in handling complex catalytic systems.

Conclusions:

AMUSE provides a seamless and automated approach for multiscale simulation of heterogeneous catalysis.
The tool facilitates a comprehensive computational investigation from atomistic to reactor scales.
AMUSE is essential for informed catalyst design and performance prediction.