Knowledge Engineering for Materials Science
About
Our research group applies knowledge representation methods in the field of materials science research. We focus on developing semantic artifacts and software tools that facilitate data-driven approaches. By using these methods, we create more informed and intuitive data structures while ensuring the generation of FAIR (Findable, Accessible, Interoperable, Reusable) data. Our overarching goal is to accelerate materials design, and develop reasoning and decision-making systems for materials science.
Research Topics
Our research focuses on developing ontologies for materials science and integrating them into computational workflows to annotate and structure materials data. This approach enables the embedding of physical concepts directly into data for enhanced usability.
Besides collaborating with experimental and simulational partners, we also perform own simulations: these are mainly ab initio and atomistic simulations for studying the properties of crystal defects. Based on this, we create knowledge graphs that connect structural, property, and thermodynamic information to understand structure-property relationships in materials.
