Prof. Dr. rer. nat. Karsten Reuter

Courses and Dates

Offered Bachelor’s or Master’s Theses Topics

Verzerrungen in LSPS Kristallen: Einblicke durch maschinelles Lernen

In 2011, a new solid lithium electrolyte was reported, featuring liquid-like Li-ion conduction in a crystalline solid matrix. The ultrafast room temperature transport of tetragonal Li10GeP2S12 (LGPS) with a conductivity of several mS/cm exceeds the values of most crystalline Li conductors by one order of magnitude. Accordingly, there has been a strong search in realizing LGPS-type materials based on the homologous elements Si and Sn. LXPS (X=Ge,Sn,Si) electrolytes appear in a tetragonal and orthorhombic modification. While the orthorhombic phase is an undesired inpurity in the LGPS electrolyte, orthorhombic LSPS leads to an enhancement of the conductivity. A possible explanation of the increased conductivity,
is an interplay of orthorombic and tetragonal LSPS, which distorts tetragonal LSPS. This distortion may lead to a favorable opening of Li-channels. The aim of this work is to investigate the effect of distortion on the materials conductivity for tetragonal LSPS. To do so a machine-learned force-field will be provided. The project will start with the construction of distorted LSPS structures and will then focus on Molecular Dynamics (MD) calculations for appropriate LSPS ensembles at finite temperatures. Finally, the conductivities will be calculated via the Nernst-Einstein relation.

suitable as

• Bachelor’s Thesis Physics
• Master’s Thesis Condensed Matter Physics
• Master’s Thesis Applied and Engineering Physics

Supervisor: Karsten Reuter

Wie verrauscht sind eigentlich Elektronenstrukturtheorie-Daten?

The combination of quantum mechanical calculations and machine-learning techniques has led to massive advances in the high-throughput computational screening of materials and molecules. Here, it is frequently stated that training data obtained from quantum mechanical simulations (e.g. using density functional theory) is 'noise-free', because the calculations can be numerically converged to very high precision. However, this assumes that the self-consistent algorithm used to calculate the Kohn-Sham determinant always converges to the correct ground-state solution, which is by no means guaranteed. The goal of this project is to quantify the noise introduced into DFT data by different choices of intital guess and convergence acceleration. To this end, the true ground-state solutions for a representative set of molecules will be determined using wavefunction stability analysis. This benchmark will allow the development of an effective 'black-box' method for obtaining well-converged training data. Furthermore, the effect of noise on quantum mechanical machine-learning applications will be studied.

The candidate should be interested learning the fundamentals of density functional theory and supervised machine learning. The project will require the development of automated workflows for high-throughput electronic structure calculations and data analysis. Experience with a scripting language (e.g. Python) and UNIX operating systems is advantageous but not mandatory.

Questions about the project can be directed to johannes.margraf@ch.tum.de.

suitable as

• Bachelor’s Thesis Physics
• Master’s Thesis Condensed Matter Physics
• Master’s Thesis Applied and Engineering Physics

Supervisor: Karsten Reuter