Elkot, M.; Sun, B.; Ponge, D.; Raabe, D.: Strategizing for hydrogen embrittlement by protecting the weakest microstructural element. ECF24 - European Conference on Fracture 2024 in Croatia, Zagreb, Croatia (2024)
Zhou, X.; Hickel, T.; Gault, B.; Ophus, C.; Liebscher, C.; Dehm, G.; Raabe, D.: Exploring the Relationship Between Grain Boundary Structure and Chemical Composition at the Atomic Level. International Conference on Intergranular and Interphase Boundaries in Materials (IIB 2024), Beijing, China (2024)
Rao, Z.; Han, L.; Zhang, H.; Raabe, D.: Active learning strategies for the sustainability of structural metals. Royal Society Discussion Meeting on Sustainable Metals: Science and Systems, London, UK (2024)
Zhou, X.; Wei, S.; Raabe, D.: Segregation-Driven Mechanics of White Gold at the Nanoscale: A Cursing or Blessing? Schöntal Symposium on Dislocation-based Plasticity 2024, Kloster Schöntal, Germany (2024)
Umate, K. S.; Bai, Y.; Svendsen, B.; Raabe, D.: Phase-field model for Hydrogen based direct reduction of iron oxides: Role of porosity. TMS - Algorithm Development in Materials Science and Engineering, Orlando, FL, USA (2024)
Raabe, D.: Transport and phase transformations phenomena in sustainable hydrogen-based steel production. 87th Spring Meeting of the German Physical Society, Berlin, Germany (2024)
Feng, S.; Gong, Y.; Neugebauer, J.; Raabe, D.; Liotti, E.; Grant, P. S.: Multi-technique investigation of Fe-rich intermetallic compounds for more impurity-tolerant Al alloys. Annual Meeting of DPG and DPG-Frühjahrstagung (DPG Spring Meeting) of the Condensed Matter Section (SKM) 2024, Berlin, Germany (2024)
Raabe, D.: Basic Materials Science Aspects of Green Metal Production. Royal Society Conference on Sustainable Metals: Science and Systems, London, UK (2024)
Raabe, D.: The Interplay of Lattice Defects and Chemistry at Atomic Scale and Why it Matters for the Properties of Materials. Van Horn Distinguished Lecturer Series, Cleveland, OH, USA (2023)
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…