Raabe, D.; Ponge, D.; Kuzmina, M.; Sandlöbes, S.: Phase transformations at dislocations. Workshop Possibilities and Limitations of Quantitative Materials Modeling and Characterization, Bernkastel, Germany (2015)
Morsdorf, L.; Tasan, C. C.; Ponge, D.; Raabe, D.: Lath martensite transformation, µ-plasticity and tempering reactions: potential TEM aids. Seminar at Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany (2015)
Kuzmina, M.; Herbig, M.; Ponge, D.; Sandlöbes, S.; Raabe, D.: Linear Complexions: Confined Chemical and Structural States at Dislocations in Metallic Alloys. MRS Fall Meeting & Exhibit, Boston, MA, USA (2015)
Tarzimoghadam, Z.; Ponge, D.: Hydrogen Embrittlement and Sour Gas Corrosion in Oil and Gas Industry. Workshop: Hydrogen Embrittlement and Sour Gas Corrosion, Max-Planck-Institut für Eisenforschung, Düsseldorf, Germany (2015)
Herbig, M.; Ponge, D.; Gault, B.; Borchers, C.; Raabe, D.: Segregation and phase transformation at dislocations during aging in a Fe-9%Mn steel studied by correlative TEM-atom probe tomography. MSE 2014, Darmstadt, Germany (2014)
Li, Y.; Ponge, D.; Choi, P.-P.; Raabe, D.: Segregation of boron at prior austenite grain boundaries in a quenched steel studied by atom probe tomography. Atom Probe Tomography & Microscopy 2014, Stuttgart, Germany (2014)
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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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…