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Updated: May 13, 2025

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

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

Published on: April 12, 2019

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

Olga Klimanova¹, Nikita Rybin^1,2, Alexander Shapeev^1,2

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

Physical Chemistry Chemical Physics : PCCP

|April 15, 2025

Summary

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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Last Updated: May 13, 2025

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

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Published on: April 12, 2019

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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.