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Accelerating Global Search of Large-Sized Silver Clusters Using Cluster Graph Attention Network.

Li Fu1, Qiuying Du2, Linwei Sai3

  • 1Key Laboratory of Materials Modification by Laser, Ion and Electron Beams (Dalian University of Technology), Ministry of Education, Dalian 116024, China.

The Journal of Physical Chemistry Letters
|August 30, 2024
PubMed
Summary

Researchers utilized deep learning and genetic algorithms to discover the stable structures of silver clusters (Agn, 30-60 atoms). This approach explains the unusual stability of Ag48 clusters, offering a faster, accurate method for atomic structure prediction.

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Area of Science:

  • Computational chemistry and materials science.
  • Nanotechnology and cluster physics.

Background:

  • Understanding silver cluster stability is crucial, with traditional models explaining "magic numbers" but not all observed phenomena.
  • Experimental data suggests enhanced stability in silver clusters with 48 valence electrons, a property not fully explained by existing theories.

Purpose of the Study:

  • To determine the global minimum structures of silver clusters (Agn, n=30-60) using advanced computational methods.
  • To elucidate the structural and electronic properties governing the stability of these silver clusters.
  • To explain the experimentally observed enhanced stability of Ag48 clusters.

Main Methods:

  • Employed a deep learning model, cluster graph attention network (CGANet), integrated with a comprehensive genetic algorithm (CGA).
  • Utilized graphics processing unit (GPU) acceleration for efficient global structure searching.
  • Calculations were validated against density functional theory (DFT) for accuracy.

Main Results:

  • Identified global minimum structures and representative isomers for Agn clusters (n=30-60).
  • Revealed competing structural motifs, including truncated octahedra and icosahedra, and an icosahedra-based growth mode for larger clusters.
  • Demonstrated that the size-dependent evolution of structural and electronic properties explains the enhanced stability of Ag48.

Conclusions:

  • The developed CGANet combined with CGA is a highly efficient and accurate tool for exploring atomic potential energy surfaces.
  • The study provides a comprehensive understanding of structural evolution and stability trends in medium-sized silver clusters.
  • The findings successfully rationalize the puzzling enhanced stability observed in Ag48 clusters.