Structure of an Ultrathin Oxide on Pt3Sn(111) Solved by Machine Learning Enhanced Global Optimization. Issue 25 (29th April 2022)

• Record Type: Journal Article
• Authors: Merte, Lindsay R.; Bisbo, Malthe Kjær; Sokolović, Igor; Setvín, Martin; Hagman, Benjamin; Shipilin, Mikhail; Schmid, Michael; Diebold, Ulrike; Lundgren, Edvin; Hammer, Bjørk
• Is Part Of: Angewandte Chemie international edition. Volume 61:Issue 25(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-29
• Subjects: Density Functional Calculations -- Machine Learning -- Structure Elucidation -- Surface Chemistry; Chemistry -- Periodicals; 540
• DOI: 10.1002/anie.202204244
• Languages: English
• ISSNs: 1433-7851

Abstract

Abstract: 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 Pt3 Sn(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. Abstract : 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 Pt3 Sn(111) which had eluded determination by experimental methods.