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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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Accelerating the global search of adsorbate molecule positions using machine-learning interatomic potentials with

Olga Klimanova1, Nikita Rybin1,2, Alexander Shapeev1,2

  • 1Skolkovo Institute of Science and Technology, Moscow, Russian Federation. alexander@shapeev.com.

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This summary is machine-generated.

This study introduces a machine learning algorithm to speed up finding molecule adsorption sites on surfaces. The method accurately predicts surface adsorbate geometries, matching literature results for various catalytic systems.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Surface Science

Background:

  • Determining molecule adsorption sites on surfaces is crucial for catalysis.
  • Accurate prediction of surface adsorbate geometries is computationally demanding.

Purpose of the Study:

  • To develop an accelerated algorithm for identifying molecule adsorption sites.
  • To improve the efficiency of global optimization for surface adsorbate geometries.

Main Methods:

  • Utilized a machine-learning interatomic potential (moment tensor potential) to approximate the potential energy surface.
  • Employed an active learning algorithm for automated training dataset construction.
  • Validated the methodology on diverse catalytic systems (CO/Pd(111), NO/Pd(100), NH3/Cu(100), C6H6/Ag(111), CH2CO/Rh(211)).

Main Results:

  • The algorithm successfully accelerated the search for adsorption sites.
  • Predicted surface adsorbate geometries showed agreement with existing literature data.
  • The approach proved effective across various surface crystallographic orientations and adsorbate types.

Conclusions:

  • The developed algorithm offers an efficient and accurate method for predicting molecule adsorption sites.
  • This approach can significantly aid research in catalysis and surface science.
  • Machine learning potentials combined with active learning provide a powerful tool for computational materials discovery.