Kim, J.-K.; Guo, W.; Choi, P.-P.; Raabe, D.: Compositional evolution of long-period stacking ordered structures in magnesium studied by atom probe tomography. Scripta Materialia 156, pp. 55 - 59 (2018)
Palanisamy, D.; Raabe, D.; Gault, B.: Elemental segregation to twin boundaries in a MnAl ferromagnetic Heusler alloy. Scripta Materialia 155, pp. 144 - 148 (2018)
Wang, D.; Diehl, M.; Roters, F.; Raabe, D.: On the role of the collinear dislocation interaction in deformation patterning and laminate formation in single crystal plasticity. Mechanics of Materials 125, pp. 70 - 79 (2018)
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.