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Related Concept Videos

Catalysis02:50

Catalysis

26.7K
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.
26.7K
Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

3.2K
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...
3.2K
Reduction of Alkenes: Catalytic Hydrogenation02:13

Reduction of Alkenes: Catalytic Hydrogenation

11.8K
Alkenes undergo reduction by the addition of molecular hydrogen to give alkanes. Because the process generally occurs in the presence of a transition-metal catalyst, the reaction is called catalytic hydrogenation.
Metals like palladium, platinum, and nickel are commonly used in their solid forms — fine powder on an inert surface. As these catalysts remain insoluble in the reaction mixture, they are referred to as heterogeneous catalysts.
The hydrogenation process takes place on the...
11.8K
Reduction of Benzene to Cyclohexane: Catalytic Hydrogenation01:28

Reduction of Benzene to Cyclohexane: Catalytic Hydrogenation

4.3K
Unlike the easy catalytic hydrogenation of an alkene double bond, hydrogenation of a benzene double bond under similar reaction conditions does not take place easily. For example, in the reduction of stilbene, the benzene ring remains unaffected while the alkene bond gets reduced. Hydrogenation of an alkene double bond is exothermic and a favorable process. In contrast, to hydrogenate the first unsaturated bond of benzene, an energy input is needed; that is, the process is endothermic. This is...
4.3K

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Related Experiment Video

Updated: Jun 6, 2025

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

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Modeling Heterogeneous Catalysis Using Quantum Computers: An Academic and Industry Perspective.

Seenivasan Hariharan1,2, Sachin Kinge3, Lucas Visscher4

  • 1Institute for Theoretical Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.

Journal of Chemical Information and Modeling
|November 29, 2024
PubMed
Summary

Quantum computing offers a paradigm shift for modeling heterogeneous catalysis, overcoming limitations of traditional methods like Density Functional Theory (DFT) for designing sustainable industrial catalysts.

Keywords:
density functional theoryembedding techniquesheterogeneous catalysis modelingmagnetic catalystsquantum computing algorithmsquantum-centric computingspin-related phenomenastrong correlation effectsuncertainty quantificationvariational quantum algorithms

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Engineering

Background:

  • Heterogeneous catalysis is vital for sustainable fuel, chemical, and pharmaceutical production.
  • Current methods struggle to model complex catalyst-reactant interactions, hindering optimal catalyst discovery.
  • Density Functional Theory (DFT) has been a primary tool but has limitations.

Purpose of the Study:

  • To review the application of quantum computing algorithms in heterogeneous catalysis modeling.
  • To highlight quantum computing's potential to address DFT limitations, especially for emerging materials.
  • To explore hybrid quantum-classical approaches for large-scale modeling.

Main Methods:

  • Review of quantum computing algorithms and their relevance to catalysis.
  • Discussion of emerging materials (alloys, single-atom, magnetic catalysts).
  • Exploration of embedding strategies combining quantum and classical methods.

Main Results:

  • Quantum computing can potentially overcome DFT's limitations in strong correlation and spin effects.
  • Emerging materials require advanced modeling techniques beyond traditional DFT.
  • Hybrid quantum-classical approaches offer a viable path for complex systems.

Conclusions:

  • Quantum computing promises a paradigm shift in understanding catalytic interfaces.
  • Integration of quantum algorithms will advance computational chemistry and catalyst design.
  • Growing academic and industrial investment signals a transformative future for catalysis research.