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Related Concept Videos

Catalysis02:50

Catalysis

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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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Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

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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...
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Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
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Reduction of Alkenes: Catalytic Hydrogenation02:13

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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...
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Regioselectivity and Stereochemistry of Acid-Catalyzed Hydration02:34

Regioselectivity and Stereochemistry of Acid-Catalyzed Hydration

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The rate of acid-catalyzed hydration of alkenes depends on the alkene's structure, as the presence of alkyl substituents at the double bond can significantly influence the rate.
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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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Invariant Molecular Representations for Heterogeneous Catalysis.

Jawad Chowdhury1, Charles Fricke2, Olajide Bamidele2

  • 1Department of Computer Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States.

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This study introduces a novel machine learning method using invariant molecular representations to accurately predict catalyst adsorption energies. This approach significantly improves upon traditional density functional theory calculations for heterogeneous catalyst development.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Catalyst screening is crucial for chemical processes.
  • Density functional theory (DFT) is widely used but computationally expensive.
  • Predicting adsorption energies for complex chemistries is challenging.

Purpose of the Study:

  • To develop a novel machine learning (ML) method for predicting adsorption energies.
  • To utilize invariant molecular representations (IMRs) for improved accuracy.
  • To overcome the computational cost and limitations of traditional DFT methods.

Main Methods:

  • Extracted molecular fingerprints for reaction intermediates.
  • Employed a Siamese neural network for training.
  • Learned invariant molecular representations (IMRs) across multiple DFT functionals.

Main Results:

  • The IMR method demonstrated superior performance in predicting adsorption energies.
  • Achieved lowest mean absolute errors (MAEs) across various DFT functionals (e.g., 0.10 eV for BEEF-vdW).
  • Validated efficacy for propane dehydrogenation on platinum catalyst surfaces.

Conclusions:

  • The proposed ML paradigm offers an effective, robust, and dependable approach for predicting adsorption energies.
  • IMRs learned via Siamese networks significantly enhance predictive accuracy.
  • This method accelerates heterogeneous catalyst discovery and development.