Li, Y.; Herbig, M.; Goto, S.; Raabe, D.: Atomic scale characterization of white etching area and its adjacent matrix in a martensitic 100Cr6 bearing steel. Materials Characterization 123, pp. 349 - 353 (2017)
Lübke, A.; Loza, K.; Patnaik, R.; Enax, J.; Raabe, D.; Prymak, O.; Fabritius, H.-O.; Gaengler, P.; Epple, M.: Reply to the ‘Comments on “Dental lessons from past to present: ultrastructure and composition of teeth from plesiosaurs, dinosaurs, extinct and recent sharks”’ by H. Botella et al., RSC Adv., 2016, 6, 74384–74388. RSC Advances 7 (11), pp. 6215 - 6222 (2017)
Baron, C.; Springer, H.; Raabe, D.: Combinatorial screening of the microstructure–property relationships for Fe–B–X stiff, light, strong and ductile steels. Materials and Design 112, pp. 131 - 139 (2016)
Baron, C.; Springer, H.; Raabe, D.: Effects of Mn additions on microstructure and properties of Fe–TiB2 based high modulus steels. Materials and Design 111, pp. 185 - 191 (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
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…
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…