Morsdorf, L.; Ponge, D.; Raabe, D.; Tasan, C. C.: New multi-probe experimental approaches to study complex lath martensite. Seminar at Department of Mechanical Engineering, Kyushu University, Fukuoka, Japan (2016)
Raabe, D.; Choi, P.-P.; Gault, B.; Ponge, D.; Yao, M.; Herbig, M.: Segregation engineering for self-organized nanostructuring of materials - from atoms to properties? APT&M 2016 - Atom Probe Tomography & Microscopy 2016 (55th IFES) , Gyeongju, South Korea (2016)
Kuzmina, M.; Gault, B.; Herbig, M.; Ponge, D.; Sandlöbes, S.; Raabe, D.: From grains to atoms: ping-pong between experiment and simulation for understanding microstructure mechanisms. Res Metallica Symposium, Department of Materials Engineering, KU Leuven, Leuven, The Netherlands (2016)
Ponge, D.; Herbig, M.; Tasan, C. C.; Raabe, D.: Integrated experimental and simulation analysis of dual phase steels. Workshop on Possibilities and Limitations of Quantitative Materials Modeling and Characterization 2016, Bernkastel, Germany (2016)
Raabe, D.: Materials Engineering through the Ages: from the Battle of Kadesh to Atomic Scale Materials Design. Elite Network of Bavaria (ENB) Forum in Erlangen: Focus on Materials Engineering, Erlangen, Germany (2016)
An, D.; Konijnenberg, P. J.; Zaefferer, S.; Raabe, D.: Correlation between the 5-parametric GBCD and the corrosion resistance of a 304 stainless steel by 3D-EBSD. RMS-EBSD Meeting 2016, Manchester, UK (2016)
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…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…