The new Energy Materials Acceleration Platform (E-MAP) at the Karlsruhe Institute of Technology (KIT) enables a systematic examination, evaluation, and targeted refining of thousands of material variants. It links automated experiments with precise material characterization, thus creating the foundation for the accelerated development of energy materials.


Developing new functional materials often requires testing numerous material combinations. Conventional experiments reach their limits quickly if many variants are to be compared. To overcome this bottleneck, the new Energy Materials Acceleration Platform (E-MAP) at KIT automates central steps in the lab. “Robot systems perform tasks such as preparing materials, handling samples, thin-film deposition and sample characterization,” said Dr. Holger Röhm from KIT’s Light Technology Institute (LTI), whose research team has set up the platform. “This allows us to conduct experiments with unprecedented precision and reproducibility. With the E-MAP, we can quickly identify which material compositions and production conditions are particularly promising for a specific application."


A Modular Design with Flexible Expansion Options

E-MAP is part of a self-contained system. It can be used to process sensitive materials under controlled conditions. The platform produces thin films from solution-based source materials. A microfluidic system enables the automated synthesis and formulation of semiconductor inks. The researchers have continuously added new thin-film characterization techniques. “An essential advantage of E-MAP is its modular design,” said Professor Alexander Colsmann from KIT’s LTI. “We are able to integrate new experiments and characterization methods and thus adapt the platform to different scientific problems. It is also open to cooperation partners from science and industry who can input proprietary methods and equipment.”


From Automated Experiments to AI-based Materials Development

Automation creates large amounts of experimental data. The researchers are aiming to evaluate this data using AI methods that help them to identify promising material combinations early through virtual simulations and to control autonomous or semi-autonomous screening processes. To this end, the researchers integrate various automated research platforms. “Linking synthesis, processing, characterization, and data evaluation creates a research process that enables us to plan and conduct experiments based on data more effectively,” said Röhm.