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
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.