As the world transitions towards electrification and emission-free processes, there’s a growing need to discover and scale up materials and processes for energy storage and conversion. Our team is dedicated to advancing research by leveraging tools like machine learning, autonomous robotics, and data science. We focus not only on optimizing individual materials, components, and processes but also on developing complete material systems, such as batteries and chemical reactors. Additionally, we explore new methods to enhance the research process itself.
We are an experimental group specializing in high-throughput systems for synthesis, characterization, assembly, and recycling. By integrating automated experiments with theoretical frameworks, we address the entire research lifecycle—from materials synthesis to system integration. Our projects include the development of combinatorial synthesis machines, high-throughput characterization tools, agile manufacturing equipment, parallel chemical reactors, lab automation systems, Bayesian optimization methods, and explainable machine learning models.
Primarily based within the TUM School of Natural Sciences and aligned with the Technical Chemistry section, we also collaborate closely with the Munich Data Science Institute (MDSI) and the Munich Institute for Robotics and Machine Intelligence (MIRMI).
We meet every Wednesday at 10:30 AM in a hybrid format. If you’re interested in presenting, please contact Qiaomin Ke.
Prospective Students
We are always looking for enthusiastic students to join our team. If you’re interested, please reach out to Helge Stein or the representative PhD student in your field of interest for project suggestions.
