Data-guided experimentation and machine learning (A05)

• Subject Area: Computer-Aided Design of Materials and Simulation of Materials Behaviour from Atomic to Microscopic Scale ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 506711657

A05 is situated at the intersection of all CRC projects that generate materials data, both from experiment and simulation, to fuse the information contained in disparate data sources to leverage synergetic effects and offer a complete and accurate representation of the knowledge contained in the combined data from all projects. This is achieved by (i) assistance with and provisioning of pre- and post-processing tools related to the common data infrastructure (see INF project) and (ii) analysing the information contained in the combined dataset to (iii) provide guidance to the next set of measurements or simulations to minimise knowledge uncertainty and (iv) formulate design principles for CCSS based on machine learning algorithms for catalysis.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1625: Atomic-scale understanding and design of multifunctional compositionally complex solid solution surfaces
• Applicant Institution: Ruhr-Universität Bochum
• Project Head: Professor Dr.-Ing. Markus Stricker, since 1/2024