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Bird's-Eye View of the Activity Distribution on a Catalyst Surface via a Machine Learning-Driven Adequate Sampling

Hui Yang^1,2,3, Pengju Ren^1,2, Xiaobin Geng²

¹State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China.

The Journal of Physical Chemistry Letters

|April 25, 2024

Summary

This study introduces a machine learning framework for catalyst design, improving understanding of active sites. It enables statistical insights into catalyst surfaces for enhanced chemical reactions like ammonia synthesis.

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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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Published on: April 12, 2019

Area of Science:

Catalysis
Materials Science
Computational Chemistry

Background:

Understanding catalyst active centers is crucial for rational catalyst design.
Characterizing the structure and activity distribution of active sites remains a challenge in catalysis.
Accurate theoretical and experimental investigations are needed to address these challenges.

Purpose of the Study:

To develop a machine learning-driven adequate sampling (MLAS) framework.
To obtain a statistical understanding of the chemical environment near catalyst active sites.
To apply the MLAS framework to the N2 activation process in ammonia synthesis.

Main Methods:

Implemented combined strategies for efficient sampling: decomposition of degrees of freedom, stratified sampling, Gaussian process regression, and constraint optimization.
Developed a machine learning-driven adequate sampling (MLAS) framework.
Utilized computational methods to analyze the N2 activation step.

Main Results:

Calculated the population function, PA, providing a comprehensive understanding of active centers.
Demonstrated the MLAS framework's ability to statistically characterize catalyst active sites.
Successfully applied the framework to the rate-determining step of ammonia synthesis.

Conclusions:

The MLAS framework offers a novel approach for statistically understanding catalyst active centers.
This method provides intuitive insights into the distribution and nature of active sites.
The MLAS framework shows broad applicability to complex catalytic materials and reaction networks.