Computational Discovery of Novel Materials and Structures - Going Beyond Crystals
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Europe/Zurich
Description
Determining the atomic structure of novel materials and phases is a problem of great importance in many areas of physics, chemistry and materials science and forms a cornerstone of the emerging field of Materials Informatics. I will give an overview of the ab initio random structure searching (AIRSS) approach and illustrate how it can be used to predict the atomic structure of materials, ranging from bulk crystals to lower dimensional systems and defects, such as interfaces. The method relies on generating random structures, followed by geometry optimization within the framework of density functional theory. Several systems, including the bulk ternary compounds of Ni-In-As and the phases of 2D monolayer ice/water will be shown as examples to introduce the method and illustrate its wide applicability. The main part of this talk will focus on how AIRSS can be applied to interface structure prediction: Interfaces are very hard to predict due to the complicated geometries, crystal orientations and possible non-stoichiometric conditions involved and provide a major challenge to structure prediction. A detailed understanding of and ability to predict the atomic structure of interfaces is however of crucial importance for many technologies. Examples of several grain boundary defects will be presented, including grain boundaries in 2D materials (graphene), as well as much more complex 3D systems such as grain boundaries in transition metal oxides (SrTiO3, TiO2). Direct comparison to experimental results will be made.
