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Related Concept Videos

Heterogeneous Catalysis01:22

Heterogeneous Catalysis

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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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Introduction to Mechanisms of Enzyme Catalysis01:13

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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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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

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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.
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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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Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis.

Oliver Loveday1,2, Kamila Kaźmierczak3, Núria López1

  • 1Institute of Chemical Research of Catalonia (ICIQ-CERCA), The Barcelona Institute of Science and Technology, Av. Països Catalans 16, Tarragona 43007, Spain.

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Summary

Machine learning interatomic potentials (MLIPs) offer a paradigm shift in computational catalysis, matching density functional theory (DFT) accuracy at lower costs. This perspective explores MLIPs as tools for heterogeneous catalysis, addressing challenges for widespread adoption.

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MLIPsRWGSfoundational modelsheterogeneous catalysismachine learning

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Catalysis

Background:

  • Accurate modeling of surface reactivity is crucial for catalyst design.
  • Density functional theory (DFT) is the primary computational method for atomistic understanding.
  • DFT calculations are computationally expensive.

Purpose of the Study:

  • To provide an overview of state-of-the-art machine learning interatomic potentials (MLIPs) for heterogeneous catalysis.
  • To assess MLIPs as "out-of-the-box" tools for catalysis research.
  • To discuss the potential of MLIPs to democratize computational catalysis.

Main Methods:

  • Summarize different families of MLIPs and their training processes.
  • Apply pretrained MLIP models to heterogeneous catalysis problems.
  • Critically evaluate model transferability and integration challenges.

Main Results:

  • MLIPs show potential to match DFT accuracy with significantly reduced computational cost.
  • Pretrained MLIPs can be applied to heterogeneous catalysis problems.
  • Challenges remain in model transferability, standardization, and achieving reliable predictive power.

Conclusions:

  • MLIPs represent a significant advancement in computational catalysis.
  • Standardized protocols are needed to benchmark MLIP performance.
  • Further development is required to overcome hurdles for widespread, reliable use of MLIPs.