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Cascading symmetry constraint during machine learning-enabled structural search for sulfur-induced Cu(111)-(43×43)
Florian Brix1, Mads-Peter Verner Christiansen1, Bjørk Hammer1
1Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, DK-8000 Aarhus C, Denmark.
The Journal of Chemical Physics
|May 15, 2024
Summary
Exploiting symmetry in structural modeling accelerates atomistic structure optimization. A new cascading symmetry search method efficiently finds stable surface reconstructions, identifying a novel structure for sulfur-induced Cu (111).
Area of Science:
- Materials Science
- Computational Chemistry
- Surface Science
Background:
- Global atomistic structure optimization is computationally intensive.
- Symmetry in structural models can be leveraged for efficiency.
- Understanding surface reconstructions is crucial for materials properties.
Purpose of the Study:
- To investigate the use of symmetry in accelerating global atomistic structure optimization.
- To develop a novel search strategy based on gradually reducing model symmetry.
- To apply the method to identify stable surface reconstructions, specifically for sulfur-induced Cu (111).
Main Methods:
- Proposed a cascading symmetry search algorithm.
- Started models from high symmetry configurations and evolved them to lower symmetry.
- Applied the method to known surface reconstructions and the sulfur-induced Cu (111) (43×43) reconstruction.
Main Results:
- The cascading symmetry search demonstrated high efficiency for various surface reconstructions.
- A new, highly stable structure was identified for the sulfur-induced Cu (111) surface reconstruction.
- The identified structure aligns with existing experimental evidence.
Conclusions:
- Exploiting symmetry is an effective strategy to speed up atomistic structure optimization.
- The cascading symmetry search is a powerful tool for discovering stable surface structures.
- The new structure for sulfur-induced Cu (111) provides valuable insights into surface phenomena.
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