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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
Published on: April 12, 2019
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Kinetic modelling of heterogeneous catalytic systems
1University College London, Torrington Place, London WC1E 7JE, UK.
Journal of Physics. Condensed Matter : an Institute of Physics Journal
|November 14, 2014
Summary
Heterogeneous catalysis is vital for modern products. Kinetic Monte Carlo (KMC) simulations accelerate catalytic materials discovery by modeling complex systems, reducing trial-and-error experiments.
Area of Science:
- Chemistry
- Materials Science
- Computational Science
Background:
- Heterogeneous catalysis is crucial for numerous modern technologies and products.
- Discovering new catalytic materials often involves time-consuming trial-and-error experimentation.
- First-principles-based kinetic modeling offers a promising alternative for understanding and discovering catalysts.
Purpose of the Study:
- To review the theory and computational implementations of kinetic Monte Carlo (KMC) simulations in catalysis.
- To highlight the evolution of computational catalysis over recent decades.
- To discuss current challenges and future opportunities in the field.
Main Methods:
- Summarizing the theoretical underpinnings of KMC simulation.
- Presenting recent computational implementations of KMC for catalytic systems.
- Reviewing early achievements and latest studies of realistic catalytic systems.
Main Results:
- KMC simulations can effectively integrate complexities found in catalytic systems.
- KMC has been successfully applied to unravel the physics of various catalytic systems.
- The field of computational catalysis has advanced significantly, transforming catalyst design.
Conclusions:
- Kinetic Monte Carlo simulation is a powerful tool for understanding catalytic function and aiding materials discovery.
- Continued development in computational methods promises further advancements in heterogeneous catalysis.
- Addressing current challenges will unlock new opportunities in computational catalysis research.
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