Research Group Helge Stein
Helge Stein
is head of the Chair Digital Catalysis at TU Munich.
His chair develops experimental and computer-aided methods for the accelerated discovery, characterization, and upscaling of new and improved materials in catalysis and for secondary batteries. Experimental data is collected using self-developed robots, planned and evaluated using algorithms and machine learning, and stored in a semantically searchable manner through data management. The goal is to establish a global decentralized material acceleration platform (MAP) ranging from discovery to production.
Publications @MCML
2025
Integrating Automated Electrochemistry and High-Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin-Film Lithium Battery Anodes.
Advanced Energy Materials Early Access.2404961 (Jan. 2025). DOI
Abstract
High-performance batteries need accelerated discovery and optimization of new anode materials. Herein, we explore the Si─Ge─Sn ternary alloy system as a candidate fast-charging anode materials system by utilizing a scanning droplet cell (SDC) as an autonomous electrochemical characterization tool with the goal of subsequent upscaling. As the SDC is performing experiments sequentially, an exploration of the entire ternary space is unfeasible due to time constraints. Thus, closed-loop optimization, guided by real-time data analysis and sequential learning algorithms, is utilized to direct experiments. The lead material identified is scaled up to a coin cell to validate the findings from the autonomous millimeter-scale thin-film electrochemical experimentation. Explainable machine learning (ML) models incorporating data from high-throughput Raman spectroscopy and X-ray diffraction (XRD) are used to elucidate the effect of short and long-range ordering on material performance.
MCML Authors
©all images: LMU | TUM