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Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic

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Summary

We developed a computational framework to predict the stability of single-atom and subnanometer metal clusters on catalyst supports. This method accurately determines low-energy structures, crucial for designing efficient catalysts.

Keywords:
catalyst structurecluster expansiongenetic algorithmsingle-atom catalysissubnanometer catalysis

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Catalysis

Background:

  • Single-atom catalysts (SACs) offer efficient noble metal use but their active site morphology is difficult to determine experimentally.
  • Subnanometer clusters present complex structural and energetic landscapes, challenging direct computational analysis.

Purpose of the Study:

  • To develop a computational framework for determining the structures of single-atom and subnanometer metal clusters on catalyst supports.
  • To investigate the low-energy structures of palladium (Pd) clusters on ceria (CeO2) supports, relevant to automotive catalysts.

Main Methods:

  • Utilized density functional theory (DFT) data to train a 3D cluster expansion model based on statistical learning.
  • Employed a Metropolis Monte Carlo-based genetic algorithm to identify stable and metastable cluster structures at 300 K.
  • Developed a surrogate structure-energy model correlating energy per atom with coordination number.

Main Results:

  • Identified low-energy structures for Pd clusters (n=1-21) supported on CeO2(111).
  • Observed single atoms sintering into bilayer clusters and noted similarities in shape and energy for larger cluster isomers.
  • Demonstrated the significant influence of the support material and microstructure on cluster stability.

Conclusions:

  • The developed computational framework enables accurate structure determination for subnanometer metal catalysts.
  • The findings provide insights into cluster stability and offer a predictive model for catalyst design.
  • This methodology addresses a critical gap in predicting the stability of supported metal catalysts in the subnanometer regime.