Cascading symmetry constraint during machine learning-enabled structural search for sulfur-induced Cu(111)-(43×43) surface reconstruction

Florian Brix; Mads-Peter Verner Christiansen; Bjørk Hammer

doi:10.1063/5.0201421

Cascading symmetry constraint during machine learning-enabled structural search for sulfur-induced Cu(111)-(43×43) surface reconstruction

J Chem Phys. 2024 May 7;160(17):174107. doi: 10.1063/5.0201421.

Authors

Florian Brix¹, Mads-Peter Verner Christiansen¹, Bjørk Hammer¹

Affiliation

¹ Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, DK-8000 Aarhus C, Denmark.

PMID: 38748003
DOI: 10.1063/5.0201421

Abstract

In this work, we investigate how exploiting symmetry when creating and modifying structural models may speed up global atomistic structure optimization. We propose a search strategy in which models start from high symmetry configurations and then gradually evolve into lower symmetry models. The algorithm is named cascading symmetry search and is shown to be highly efficient for a number of known surface reconstructions. We use our method for the sulfur-induced Cu (111) (43×43) surface reconstruction for which we identify a new highly stable structure that conforms with the experimental evidence.