Why should I be interested in this Master's programme if I have a Bachelor's degree in one of the following fields:

Physics: You have enjoyed a solid theoretical education in physics and now want to apply quantum mechanics to solids and molecules to predict electronic properties from purely theoretical considerations (i.e. without experimental input). They are interested in computer science and want to learn how to develop robust and scalable numerical algorithms and programme high-performance scientific software.

Chemistry: They were always more interested in theory than in laboratory work during their chemistry studies and now want to try their hand at computational chemistry, i.e. predicting chemical bonds, reactions and (supra-)molecular properties of substances. In addition to semi-empirical density functional theory and ab-initio quantum chemical methods to characterise electronic structural features, molecular dynamics, for example, can also be used here to predict the temporal development of molecular systems under given thermodynamic conditions.

Informatics and engineering: You have a solid background in programming and numerical calculations/algorithms, but also find the natural sciences fascinating. Here you will learn how to use your previously acquired skills to implement physical and chemical software and how to draw conclusions from the calculated data to macroscopic properties of materials.

Mathematics: You have a fundamental mathematics degree behind you, but want to learn more about modelling real-world phenomena. We show you how to apply mathematical methods to problems in physics or chemistry. For example, for predicting macroscopic properties of microscopic components/parts.

Background

Computer modelling and simulation of crystals and molecular structures is already a proven method in materials science to characterise solid state and surface properties. There is practically no current scientific publication of experimentally measured data that does not include a comparison with a theoretically calculated result. This applies in particular to the broad field of solid state physics, e.g. thermodynamic and optical measurements, but also to many disciplines in chemistry that deal with (supra-)molecular bonds and various types of spectroscopy such as NMR, IR, Raman or UV/VIS.

However, the confirmation of these experimental data is not the only application of so-called "computational physics and chemistry". Another important aspect is the modelling of new materials that are either technically difficult to handle (e.g. due to radioactivity) or too expensive to be produced in the laboratory in sufficiently large quantities. The ability to screen entire classes of materials with regard to a specific property such as refractive index, heat capacity or compressibility has put computational physics in a prominent position in aerospace engineering, among others. Other application examples include the prediction of (semi-)conductor properties, the determination of kinetically vs. thermodynamically preferred reaction pathways including catalysis, the description of supramolecular complex formation, the investigation of lability versus stability under ambient conditions, the modelling of the toxicological effects of various substances and the understanding of macroscopic mechanisms by means of physical and chemical properties of the substances involved in general.

This variety of possibilities for making statements about a wide range of material and substance properties as well as complete reaction pathways based on fundamental theory is prompting industry to acquire entire farms of computer organisations, so-called high-performance computing clusters, and is accordingly looking for graduates with sound knowledge and skills in computational physics and chemistry.

Electronic Structure Theory is an ideal specialisation, especially for students of "Applied Natural Sciences" with their broadly diversified education, if they are interested in theoretical concepts and their application at the modelling level. The key competence to be taught in this specialisation is the ability to solve complex classes of problems and to familiarise oneself with new areas in a short time, rather than memorising several specific individual cases. Therefore, the knowledge acquired during this Master's programme can easily be transferred to a wide range of other problems.

A classification of the specialisation in the Master's programme "Applied Natural Science" is shown in Fig. 1, the study plan can be found in Fig. 2.

Fig. 1: Overview of the specialisations in the Master's programme

Fig. 2: Studienablaufplan MNAT Vertiefung

If you have any questions or require advice, please contact Prof. Dr J. Kortus. Further information on the Master's degree programme "Applied Natural Science" can be found here.