Team Data-based Material Design
The main focus of the working group entitled "Data-based material design" is on structural analyses with large-scale facilities, the development of structural models using computer simulation and modelling methods, and the analysis of chemical bonds in oxidic structures and their influence on the chemical and physical properties of ceramic materials. By applying machine learning and deep learning methods, new procedures for the digitalization of materials research are developed and the design of digital twins for fuel cells, gas separation membranes, solid state batteries and thermal protection layers is supported.
Research area
Structural Science at Large-Scale Facilities
Synchrotron radiation and neutron scattering methods are used for diffraction and spectroscopic experiments on ceramics, in order to investigate their crystal and possibly also amorphous structures, including tomographic analyses for the development of complete 3D models, using large-scale equipment, e.g. at the ESRF (Grenoble), DESY (Hamburg) or the FRM2 research reactor (Munich).
Research Data Management
New methods of research data management are introduced in IEK-1 in order to enable the use of existing as well as new data jointly and efficiently for the development of digital twins. In this context, the team is working as a part of the National Research Data Infrastructure (NFDI), for IEK-1 and the entire Research Centre Jülich. NFDI supports researchers in the complete acquisition, analysis, processing, storage and publication of research data with regard to the rapidly advancing digitalization in materials research and the associated increased requirements
Application of Artificial Intelligence in Material Science
New methods of research data management are introduced in IEK-1 in order to enable the use of existing as well as new data jointly and efficiently for the development of digital twins. In this context, the team is working as a part of the National Research Data Infrastructure (NFDI), for IEK-1 and the entire Research Centre Jülich. NFDI supports researchers in the complete acquisition, analysis, processing, storage and publication of research data with regard to the rapidly advancing digitalization in materials research and the associated increased requirements.
Topical Editor of the international journal CRYSTALS. Most recent Special Issue entitled "Applications of Machine Learning to the Study of Crystalline Materials":
https://www.mdpi.com/journal/crystals/special_issues/machinelearning_crystals