Computational materials scientists who work at the atomic scale using quantum physics, can predict observable properties of solids ‘from scratch’ (=ab initio, from first principles). No empirical or tunable parameters are used. This does not mean, however, that these predictions are exact. The reason is that one does not solve the original (Schrödinger) equation for the material, but a mildly simplified version of it. This inevitably introduces an error bar on the predicted properties.

When materials engineers want to use such predictions from computational materials scientists, one needs to know which size of error bars can be expected. The increasing interaction between experimental materials engineers and the community of ab initio condensed matter physics has therefore triggered a renewed interest in determining with some confidence the error bars on a variety of predicted properties.

The Center for Molecular Modeling has gained an international reputation in error bar quantification for structural (http://dx.doi.org/10.1080/10408436.2013.772503) and thermal properties (http://dx.doi.org/10.1103/PhysRevB.89.014304). In this thesis, you can build upon our expertise to tackle an entire different class of surface-related properties: surface energies and the work function. With which accuracy can quantum physics predict those properties for real materials?

Goal
This work will lead you through all areas of the periodic table, where you will calculate surface properties for low-index surfaces of the elemental materials. This systematic data set will then be compared with text book collections of experimental surface energies and work functions. You will not only be able to determine the typical error bars, you will also be able to identify classes of materials for which these methods fail, and/or you might find elemental solids where the experimental values are perhaps not as reliable as the text book tabulations suggest.

In short, you will get a lot of experience in applying ab initio methods for many different solids, and your result will contribute to the thoughtful use of ab initio predictions for experimental materials science.