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Related Concept Videos

Heterogeneous Catalysis01:22

Heterogeneous Catalysis

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...
Catalysis02:50

Catalysis

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.
Catalysis01:27

Catalysis

Catalysis influences the rate of chemical reactions by providing an alternative reaction pathway with lower activation energy. A catalyst speeds up a reaction, but it is not consumed during the process. The fundamental principle of catalysis is the ability of a catalyst to alter the reaction mechanism, often introducing a more efficient pathway than the uncatalyzed process.In a catalyzed reaction, the catalyst participates directly in the reaction mechanism. It interacts with reactants to form...
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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 a mild...
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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 a mild...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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

Published on: April 12, 2019

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?

Luuk H E Kempen1, Raffaele Cheula1, Mie Andersen1

  • 1Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Aarhus C, Denmark.

The Journal of Chemical Physics
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

Machine-learning interatomic potentials (MLIPs) show promise for materials simulation but require careful application. This study benchmarks 80 MLIPs for catalysis, revealing strengths in oxides but weaknesses in magnetic materials.

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Area of Science:

  • Computational materials science
  • Chemical physics
  • Machine learning applications

Background:

  • Machine-learning interatomic potentials (MLIPs) approximate ab initio accuracy, enabling larger-scale simulations.
  • Current MLIP benchmarks often neglect complex systems relevant to real-world applications like heterogeneous catalysis.
  • Systematic evaluation of diverse MLIPs on realistic catalytic tasks is needed.

Purpose of the Study:

  • To systematically analyze the zero-shot performance of 80 foundational MLIPs for heterogeneous catalysis tasks.
  • To evaluate MLIP accuracy across various datasets, including alloys, oxides, and interfaces.
  • To compare foundational MLIPs with task-specific models and identify limitations.

Main Methods:

  • Zero-shot performance evaluation of 80 different MLIPs.
  • Testing on datasets relevant to heterogeneous catalysis: adsorption, reactions, vacancy formation energies, and zero-point energies.
  • Analysis of performance on alloyed metals, oxides, and metal-oxide interfaces.
  • Comparison of structure relaxation effects on energy prediction error.
  • Benchmarking against task-specific models.

Main Results:

  • Foundational MLIPs demonstrate high accuracy for predicting vacancy formation energies in perovskite oxides and zero-point energies of nanoclusters.
  • Many MLIPs exhibit catastrophic failure on magnetic materials.
  • Structure relaxation during MLIP simulations often increases energy prediction errors compared to single-point evaluations.
  • Task-specific models can achieve accuracy comparable to leading foundational MLIPs.
  • No single MLIP universally excels across all tested applications.

Conclusions:

  • Current MLIPs show potential for specific catalytic applications but have significant limitations, especially with magnetic materials.
  • Users must carefully select and validate MLIPs for their specific research needs, as performance is not universal.
  • Task-specific models offer a competitive alternative in terms of accuracy for certain applications.