open access publication

Article, 2024

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

The Journal of Chemical Physics, ISSN 1089-7690, 0021-9606, Volume 160, 17, Page 174107, 10.1063/5.0201421

Contributors

Brix, Florian [1] Christiansen, Mads-Peter Verner 0000-0002-3550-8379 [1] Hammer, Bjørk 0000-0002-7849-6347 (Corresponding author) [1]

Affiliations

  1. [1] Aarhus University
  2. [NORA names: AU Aarhus University; University; Denmark; Europe, EU; Nordic; OECD]

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.

Keywords

algorithm, configuration, constraints, evidence, experimental evidence, low-symmetry model, machine, method, model, optimization, reconstruction, search, stable structure, structural model, structural optimization, structure, structure search, surface, surface reconstruction, symmetry, symmetry configuration, symmetry constraints, symmetry model

Funders

  • Danish National Research Foundation
  • The Velux Foundations

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