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Related Concept Videos

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Updated: Jul 1, 2025

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Interpreting chemisorption strength with AutoML-based feature deletion experiments.

Zhuo Li1,2, Changquan Zhao3, Haikun Wang4

  • 1University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China.

Proceedings of the National Academy of Sciences of the United States of America
|March 13, 2024
PubMed
Summary

This study uses Automatic Machine Learning (AutoML) to identify key factors influencing catalyst chemisorption energy. Geometric information of adsorption sites is found to be the most critical factor for binary alloy catalysts.

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Area of Science:

  • Catalysis Science
  • Materials Science
  • Computational Chemistry

Background:

  • Chemisorption energy is crucial for identifying optimal catalysts.
  • Catalyst complexity hinders identification of key determining factors.
  • High-throughput computational databases offer vast data for analysis.

Purpose of the Study:

  • To develop a methodology for extracting knowledge from catalyst databases.
  • To identify the pivotal physical quantities determining chemisorption energy.
  • To create accurate predictive models with minimal human intervention.

Main Methods:

  • Feature deletion experiment utilizing Automatic Machine Learning (AutoML).
  • Analysis of a high-throughput density functional theory (DFT) database.
  • Integration with instance-wise variable selection (INVASE) and explainable AI (XAI).

Main Results:

  • Local adsorption site geometry is the primary determinant of chemisorption energy for binary alloys.
  • A feature set of 21 intrinsic properties was identified.
  • Achieved a Mean Absolute Error (MAE) of 0.23 eV across diverse alloy surfaces.

Conclusions:

  • AutoML-based feature deletion is effective for complex chemical problems.
  • The developed model is stable, consistent, and predictive.
  • This approach facilitates the development of theoretically meaningful catalyst models.