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.
Team members @MCML
PhD Students
Recent News @MCML
Publications @MCML
2026
[5]
L. Merker • B. Zhang • J. Yuan • S. Ji • H. S. Stein
Insight Generation from Information-Dense Formation Protocols.
Batteries & Supercaps 9.2. Feb. 2026. DOI
Insight Generation from Information-Dense Formation Protocols.
Batteries & Supercaps 9.2. Feb. 2026. DOI
2025
[4]
S. Ji • J. Yuan • B. Zhang • A. Sanin • L. Merker • Z. Zhang • J. • H. S. Stein
Continuous Physics-Informed Learning Expedited Battery Mechanism Decoupling.
Advanced Science 13.1. Oct. 2025. DOI
Continuous Physics-Informed Learning Expedited Battery Mechanism Decoupling.
Advanced Science 13.1. Oct. 2025. DOI
[3]
S. Ji • B. Zhang • H. S. Stein • Z. Zhang • J. Zhu
Explainable Artificial Intelligence of Things for Health Prognosis of Lithium-Ion Batteries.
Energy and Fuels 39.36. Aug. 2025. DOI
Explainable Artificial Intelligence of Things for Health Prognosis of Lithium-Ion Batteries.
Energy and Fuels 39.36. Aug. 2025. DOI
[2]
L. Merker • M. Blessing • B. Zhang • H. S. Stein
Information Dense and Industry Scalable Accelerated Formation.
Advanced Intelligent Discovery. Jun. 2025. DOI
Information Dense and Industry Scalable Accelerated Formation.
Advanced Intelligent Discovery. Jun. 2025. DOI
[1]
A. Sanin • J. K. Flowers • T. H. Piotrowiak • F. Felsen • L. Merker • A. Ludwig • D. Bresser • H. S. Stein
Integrating Automated Electrochemistry and High-Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin-Film Lithium Battery Anodes.
Advanced Energy Materials 15.11. Jan. 2025. DOI
Integrating Automated Electrochemistry and High-Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin-Film Lithium Battery Anodes.
Advanced Energy Materials 15.11. Jan. 2025. DOI
©all images: LMU | TUM
Back to Top
2024-12-27 - Last modified: 2025-01-02