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

DOI: 10.1002/anie.202204244

Authors: Lindsay R. Merte (Malmo Universitet) , Malthe Kjær Bisbo (Aarhus University) , Igor Sokolović (Vienna University of Technology) , Martin Setvín (Charles University) , Benjamin Hagman (Lund University) , Mikhail Shipilin (Stockholm University) , Michael Schmid (Vienna Technical University) , Ulrike Diebold (Vienna University of Technology) , Edvin Lundgren (Lund University) , Bjørk Hammer (University of Aarhus)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Angewandte Chemie International Edition

State: Published (Approved)
Published: April 2022
Diamond Proposal Number(s): 17608

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 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.

Subject Areas: Materials, Physics, Information and Communication Technology


Instruments: I07-Surface & interface diffraction

Other Facilities: MAX IV

Added On: 06/04/2022 14:00

Documents:
Angew Chem Int Ed - 2022 - Merte - Structure of an Ultrathin Oxide on Pt3Sn 111 Solved by Machine Learning Enhanced Global.pdf

Discipline Tags:

Surfaces Artificial Intelligence Physics Information & Communication Technologies Materials Science interfaces and thin films Data processing

Technical Tags:

Diffraction