Room: ZGH 02/115
Phone: +49 (0)234-32-21439
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Felix Thelen studied mechanical engineering with a specialization in design and automation technology at the Ruhr University Bochum and completed his bachelor's degree in 2020 and his master's degree in 2022. At the end of 2022, he began his doctorate at the Chair of New Materials and Interfaces in the field of autonomous experiments. This involves using machine learning algorithms to significantly accelerate the measurement of a wide range of material properties. Since 2024, he has been working on the ERC DEMI project, which is researching novel electrocatalysts for important energy conversion reactions. The aim is to use artificial intelligence, high-throughput methods and theoretical modeling to specifically identify particularly stable and efficient material combination.
F. Thelen, R. Zehl, R. Zerdoumi, J.L.Bürgel, L. Banko, W. Schuhmann, A. Ludwig (2025)
Accelerating Combinatorial Electrocatalyst Discovery with Bayesian Optimization: a Case Study in the Quaternary System Ni-Pd-Pt-Ru for the Oxygen Evolution Reaction
Advanced Science, 2025, e07302, DOI: 10.1002/advs.202507302
F. Thelen, F. Lourens, A. Ludwig (2025)
Accelerating Surface Composition Characterization of Thin-Film Materials Libraries Using Multi-Output Gaussian Process Regression
Advanced Intelligent Discovery, 2025, 202500062, DOI: 10.1002/aidi.202500062
J.L. Bürgel, R. Zehl, F. Thelen, R. Zerdoumi, O.A. Krysiak, B. Kohnen, E. Suhr, W. Schuhmann, A. Ludwig (2025)
Exploration of nanostructured high-entropy alloys for key electrochemical reactions: a comparative study for the solid solution systems Cu-Pd-Pt-Ru, Ir-Pd-Pt-Ru and Ni-Pd-Pt-Ru
Faraday Discussions, 2025, Accepted Manuscript, DOI: 10.1039/D5FD00082C
F. Thelen, R. Zehl, J.L. Bürgel, D. Depla, A. Ludwig (2025)
A python-based approach to sputter deposition simulations in combinatorial materials science
Surface and Coatings Technology, 2025, Vol. 503, p. 131998, DOI: 10.1016/j.surfcoat.2025.131998
F. Thelen, L. Banko, R. Zehl, S. Baha, A. Ludwig (2023)
Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements
Digital Discovery, 2023, Vol. 2, p. 1612-1619, DOI: 10.1039/D3DD00125C
E. Suhr, O. Krysiak, V. Strotkötter, F. Thelen, W. Schuhmann, A. Ludwig (2023)
High-Throughput Exploration of Structural and Electrochemical Properties of the High-Entropy Nitride System (Ti–Co–Mo–Ta–W)N
Adv. Eng. Mater.2023, 2300550, DOI: 10.1002/adem.202300550
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Last update: Jul 02, 2025
