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Machine learning study of surface reconstructions of the Cu2O(111) surface

Payal Wadhwa1,*,†, Michael Schmid2, and Georg Kresse1,3

  • 1Faculty of Physics and Center for Computational Materials Science, University of Vienna, Kolingasse 14-16, A-1090 Vienna, Austria
  • 2Institute of Applied Physics, TU Wien, A-1040 Vienna, Austria
  • 3VASP Software GmbH, Berggasse 21, A-1090 Vienna, Austria

  • *Contact author: payal.wadhwa@univie.ac.at; payal_wadhwa@ iiitvadodara.ac.in
  • †Present address: Indian Institute of Information Technology Vadodara, Gandhinagar-382028, Gujarat, India.
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Phys. Rev. B 112, 205420 – Published 17 November, 2025

DOI: https://doi.org/10.1103/sfjm-1gyr

Abstract

The atomic structure of the most stable reconstructed surface of cuprous oxide (Cu2O)(111) surface has been a longstanding topic of debate. In this study, we develop on-the-fly machine-learned force fields (MLFFs) to systematically investigate the various reconstructions of the Cu2O(111) surface under stoichiometric as well as O- and Cu-deficient or rich conditions, focusing on both (3×3)R30∘ and (2×2) supercells. By utilizing parallel tempering simulations supported by MLFFs, we confirm that the previously described nanopyramidal and Cu-deficient (1×1) structures are the lowest energy structures from moderately to strongly oxidizing conditions. In addition, we identify two promising nanopyramidal reconstructions at highly reducing conditions, a stoichiometric one and a Cu-rich one. Surface energy calculations performed using spin-polarized PBE, PBE+U, r2SCAN, and HSE06 functionals show that the previously known Cu-deficient configuration and nanopyramidal configurations are at the convex hull (and, thus, equilibrium structures) for all functionals, whereas the stability of the other structures depends on the functional and is therefore uncertain. Our findings demonstrate that on-the-fly trained MLFFs provide a simple, efficient, and rapid approach to explore the complex surface reconstructions commonly encountered in experimental studies, and also enhance our understanding of the stability of Cu2O(111) surfaces.

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Phys. Rev. B 112, 205420– Published 17 November, 2025
  • Received 8 July 2025
  • Revised 24 August 2025
  • Accepted 20 October 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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