Pradeep, K. G.; Herzer, G.; Raabe, D.: Atomic scale study of CU clustering and pseudo-homogeneous Fe-Si nanocrystallization in soft magnetic FeSiNbB(CU) alloys. Ultramicroscopy 159 (2), pp. 285 - 291 (2015)
Stoffers, A.; Cojocaru-Mirédin, O.; Seifert, W.; Zaefferer, S.; Riepe, S.; Raabe, D.: Grain boundary segregation in multicrystalline silicon: correlative characterization by EBSD, EBIC, and atom probe tomography. Progress in Photovoltaics: Research and Applications 23 (12), pp. 1742 - 1753 (2015)
Pradeep, K. G.; Tasan, C. C.; Yao, M.; Deng, Y.; Springer, H.; Raabe, D.: Non-equiatomic high entropy alloys: Approach towards rapid alloy screening and property-oriented design. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 648, pp. 183 - 192 (2015)
Ma, D.; Grabowski, B.; Körmann, F.; Neugebauer, J.; Raabe, D.: Ab initio thermodynamics of the CoCrFeMnNi high entropy alloy: Importance of entropy contributions beyond the configurational one. Acta Materialia 100, pp. 90 - 97 (2015)
Pierce, D. T.; Jiménez, J. A.; Bentley, J.; Raabe, D.; Wittig, J. E.: The influence of stacking fault energy on the microstructural and strainhardening evolution of Fe–Mn–Al–Si steels during tensile deformation. Acta Materialia 100, pp. 178 - 190 (2015)
Wen, Y.; Xiao, H.; Peng, H.; Li, N.; Raabe, D.: Relationship Between Damping Capacity and Variations of Vacancies Concentration and Segregation of Carbon Atom in an Fe–Mn Alloy. Metallurgical and Materials Transactions a-Physical Metallurgy and Materials Science 46A (11), pp. 4828 - 4833 (2015)
Konijnenberg, P. J.; Zaefferer, S.; Raabe, D.: Assessment of geometrically necessary dislocation levels derived by 3D EBSD. Acta Materialia 99, pp. 402 - 414 (2015)
Choi, W. S.; De Cooman, B. C.; Sandlöbes, S.; Raabe, D.: Size and orientation effects in partial dislocation-mediated deformation of twinning-induced plasticity steel micro-pillars. Acta Materialia 98, 12304, pp. 391 - 404 (2015)
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…