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.

Machine learning techniques can be implemented to accelerate surface structure determination based on density functional theory. The application of such an algorithm is demonstrated here for a surface oxide on Pt3Sn(111) which had eluded determination by experimental methods.

Center for Interstellar Catalysis Department of Physics and Astronomy Aarhus University 8000 Aarhus Denmark

Department of Surface and Plasma Science Faculty of Mathematics and Physics Charles University 180 00 Prague 8 Czech Republic

Div of Synchrotron Radiation Research Lund University 22100 Lund Sweden

Institute of Applied Physics TU Wien 1040 Vienna Austria

Materials Science and Applied Mathematics Malmö University 20506 Malmö Sweden

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