Springer, H.; Baron, C.; Tanure, L.; Rohwerder, M.: A combinatorial study of the effect of Al and Cr additions on the mechanical, physical and corrosion properties of Fe. Materials Today Communications 29, 102947 (2021)
Yin, Y.; Schulz, M.; Rohwerder, M.: Optimizing smart self-healing coatings: Investigating the transport of active agents from the coating towards the defect. Corrosion Science 190, 109661 (2021)
Zhong, X.; Schulz, M.; Wu, C.-H.; Rabe, M.; Erbe, A.; Rohwerder, M.: Limiting Current Density of Oxygen Reduction under Ultrathin Electrolyte Layers: From the Micrometer Range to Monolayers. ChemElectroChem 8 (4), pp. 712 - 718 (2021)
Friedrichs, M.; Peng, Z.; Grunwald, T.; Rohwerder, M.; Gault, B.; Bergs, T.: PtIr protective coating system for precision glass molding tools: Design, evaluation and mechanism of degradation. Surface and Coatings Technology 385, 125378 (2020)
Sun, B.; Krieger, W.; Rohwerder, M.; Ponge, D.; Raabe, D.: Dependence of hydrogen embrittlement mechanisms on microstructure-driven hydrogen distribution in medium Mn steels. Acta Materialia 183, pp. 313 - 328 (2020)
Aguirre, J.; Walczak, M.; Rohwerder, M.: The mechanism of erosion-corrosion of API X65 steel under turbulent slurry flow: Effect of nominal flow velocity and oxygen content. WEAR 438-439, 203053 (2019)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.