We study electronic and magnetic properties of crystalline materials by using various numerical methods. Starting with density functional theory (DFT) band-structure calculations, we perform a detailed microscopic analysis based on many-body methods. Depending on the problem at hand, these are DFT+U and hybrid-functional calculations, simulations of spin models, or a DFT plus dynamical mean-field theory (DFT+DMFT) treatment. The results can be directly compared with experimental photoemission (PES, ARPES), thermodynamical (magnetic susceptibility, specific heat, magnetization), spectroscopic (ESR, NMR), and neutron scattering (diffraction, INS) data. Together with experimental colleagues, we develop an infrastructure to facilitate machine learning on data sets comprising numerical and experimental data.

Our aims are:

• a realistic description of correlated materials
• development of efficient computational workflows
• building a bridge to experiments whenever possible
• construction of a database containing computational and experimental data

We are primarily interested in the following materials:

• quantum and frustrated magnets
• correlated transition metal oxides
• oxide heterostructures
   

Our computational toolbox includes:

• DFT codes: FPLO, vasp, and wien2k
• CT-HYB impurity solvers: w2dynamics
• simulation of spin models: ALPS, HPhi
• own codes in python, C++, and bash