In the research area “Automation for Laboratories (ATLAS)”, the groups “Machine Learning for High Throughput and Mechatronics (ML4HOME)” and “Laboratory Automation – Simulation and Design (LASD)” develop solutions for autonomous evaluation of materials regarding their properties.


The research group “Machine Learning for High-Throughput Methods and Mechatronics (ML4HOME)” deals with the analysis of data being generated automatically by mechatronic systems in large amounts. Examples are automated microscopes, X-ray diffractometers, and autonomous vehicles. Our focus is the generalization of big data-sets consisting of a lot of data points (high throughput), whose extent requires mandatory automatic processing.

The group “Laboratory Automation – Simulation and Design (LASD)” focuses on the design layout and optimization of (micro-)optical and fluid dynamic components, subsystems, and systems. One goal of the research and development work is to create digital twins as a representation of the systems, in order to 1) improve production processes, 2) adapt them to other, new means of production, 3) confirm settings and find new operating points, and 4) using simulations to make performance predictions. In cooperation with the IAI working groups, an integrated development approach is pursued starting with system design and continuing through the process and device development. The methods and processes developed are used in the application fields of photonic and printed systems.