Structure of an ultrathin oxide on Pt3Sn(111) solved by machine learning enhanced global optimization

L. R. Merte, M. K. Bisbo, I. Sokolović, M. Setvín, B. Hagman, M. Shipilin, M. Schmid, U. Diebold, E. Lundgren, B. Hammer

Materials Science and Applied Mathematics, Malmö University, 20506, Malmö, Sweden
Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, 8000 Aarhus, Denmark
Institut für Angewandte Physik, TU Wien, 1040 Wien, Austria
Department of Surface and Plasma Science, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
Div. of Synchrotron Radiation Research, Lund University, 22100 Lund, Sweden

Angew. Chem. Int. Ed. 61 (2022) e202204244

Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory-based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure–the (4×4) surface oxide on Pt3Sn(111)–based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation.

Corresponding author: Lindsay Merte.

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