Prof. Dr. Helge Stein


Digital Catalysis

Academic Career and Research Areas

Prof. Stein (*1988) 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.

Professor Stein studied physics at the Georg-August University of Göttingen from 2008 to 2013 and obtained his doctorate in 2017 in high-throughput methods in mechanical engineering with summa cum laude at the Ruhr University Bochum. From 2017 until taking up his tenure track professorship in applied electrochemistry at the Karlsruhe Institute of Technology in 2020, he conducted research at the California Institute of Technology (Caltech). In 2023, Professor Stein was appointed to the professorship of Digital Catalysis at TUM.


    • ACS Engineering Au Rising Star (2023)
    • Idea Prize in the category of Innovation at KIT (2023)
    • Masao Horiba Award Honorable Mention (2021)
    • Eickhoff Prize for the Best Doctoral Thesis in Mechanical Engineering at RUB (2018)

    Stein, H., Gregoire, J.: "Progress and prospects for accelerating materials science with automated and autonomous workflows" Chem. Sci., 2019, 10, 9640-9649.


    Stein, H. et al.: "From materials discovery to system optimization by integrating combinatorial electrochemistry and data science" Curr. Op in Electrochem., 35, 101053.


    Stein, H. et al.: "Machine learning of optical properties of materials – predicting spectra from images and images from spectra" Chem. Sci., 2019, 10, 47-55.


    Zhang, B., Merker, L., Sanin, A., Stein, H.,: "Robotic cell assembly to accelerate battery research" Digital Discovery, 2022, 1, 755-762.


    Rohr, B., Stein, H. et al. "Benchmarking the acceleration of materials discovery by sequential learning" Chem. Sci., 2020, 11, 2696-2706.