Zhang, J.; Morsdorf, L.; Tasan, C. C.: Multi-probe microstructure tracking during heat treatment without an in-situ setup: Case studies on martensitic steel, dual phase steel and β-Ti alloy. Materials Characterization 111, pp. 137 - 146 (2016)
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)
Wang, M.; Tasan, C. C.; Koyama, M.; Ponge, D.; Raabe, D.: Enhancing Hydrogen Embrittlement Resistance of Lath Martensite by Introducing Nano-Films of Interlath Austenite. Metallurgical and Materials Transactions a-Physical Metallurgy and Materials Science 46 (9), pp. 3797 - 3802 (2015)
Zhang, J.; Tasan, C. C.; Lai, M.; Zhang, J.; Raabe, D.: Damage resistance in gum metal through cold work-induced microstructural heterogeneity. Journal of Materials Science 50 (17), pp. 5694 - 5708 (2015)
Morsdorf, L.; Tasan, C. C.; Ponge, D.; Raabe, D.: 3D structural and atomic-scale analysis of lath martensite: Effect of the transformation sequence. Acta Materialia 95, pp. 366 - 377 (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
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.