Overview
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).
Research Topics
Battery Testing
We incorporate the battery test as the final step in our comprehensive automated battery production and assembly platform; through charging/discharging cycles and collaboration with conventional electrochemical measurement equipment platforms, full cell principles and performance can be thoroughly explored. Furthermore, derived from different battery test outcomes of various protocols in pure experiments, more extensive databases will be generated through data-driving simulation to solve time-consuming issues of battery tests.
Recently, we have been investigating the diffusion coefficient of batteries by utilizing the Galvanostatic Intermittent Titration Technique (GITT). Understanding and calculating the battery diffusion coefficient in electrode material is critical for optimizing cell design; in this part, all intricate data processes and related workflows rely on data-driving approaches to expedite battery analysis and cooperate with the automatic high-throughput battery system. Finally, the diffusion coefficient will be comprehensively demonstrated in detail based on different cycle protocols and temperatures.
In the thermal safety field of the battery, we combine numerical modeling with the science of battery materials, enabling us to develop our battery from its mechanism instead of constantly experimenting. This way, we can significantly reduce the expense of experiment stuff and become more efficient and targeted. To better establish and develop our model, an innovative temperature test platform is built which can proficiently capture the temperature and critical parameters of our battery.
Contact person: Jun Yuan
Machine Learning for Chemistry
We love to do machine learning for chemistry beyond prediction of functional properties[1]. This includes accelerated data analysis [2], data curation [3] and, most importantly, visualisation [4]. We have been interested in human-in-the-loop machine learning for almost a decade [2]. The vision is to use some of these tools to develop a better - ML-enhanced - understanding of the underlying physicochemical processes [5]. We are active in both battery research and, more recently, catalysis. In battery research, we are mainly concerned with formation and non-linear charging schedules[6], and in catalysis we are investigating novel ways to catalyse and understand the production of high-value chemicals. We are also interested in using ML tools to explore the chemical space of stable and metastable compounds more efficiently [7].
Contact person: Joachim Czapiński, Helge Stein
High-throughput Electrode Manufacturing
A cutting-edge robotic system has been developed to produce slurries from powders and liquids through gravimetric dispensing with a 6DOF (six degrees of freedom) robotic arm. This advanced automation platform provides precise control over the mixing process, ensuring consistent slurry composition and quality. After the slurry is formulated, it can be coated onto electrodes or other substrates using the same robotic system. The drying process is also automated, enabling high-throughput production of coated electrodes. Filming of this process is integrated to study the temporal evolution during the drying stage. The automation of these steps of electrode manufacturing allows for greater consistency and reproducibility than can be achieved with hand made electrodes in a small laboratory setting, and greater flexibility and agility than a large scale manufacturing plant.
Finally, the system is capable of cutting electrodes or coated sheets into specific sizes and shapes to meet various application requirements. This highly versatile robotic system can be used in a wide range of applications, including battery manufacturing and catalytic processes. The ability to produce uniform slurries with precise composition, combined with efficient coating and drying processes, makes this setup an essential tool for modern materials research and production in our lab.
Contact persons: Leah Nuss
Parameterization
Parameter identification is a key aspect of battery modeling and simulation, enabling automation and efficiency in battery production, testing, and application processes; meanwhile, monitoring changes in key parameters facilitates effective battery fault diagnosis and health management can be carried out.
In this context, machine learning will serve as the primary method for parameter identification and weight evaluation. This approach can accelerate battery design and optimization by automatically selecting the best combination of characterization and cycle protocols. Similarly, battery research based on physics models, such as the Doyle–Fuller–Newman (DFN) framework, can also be significantly simplified by integrating data-driven approaches. This integration provides a deeper insight into understanding battery principles.
Contact person: Qiaomin Ke