Jentner, R.; Tsai, S.-P.; Welle, A.; Scholl, S.; Srivastava, K.; Best, J. P.; Kirchlechner, C.; Dehm, G.: Automated classification of granular bainite and polygonal ferrite by electron backscatter diffraction verified through local structural and mechanical analyses. Journal of Materials Research 38, pp. 4177 - 4191 (2023)
Gallardo-Basile, F.-J.; Roters, F.; Jentner, R.; Best, J. P.; Kirchlechner, C.; Srivastava, K.; Scholl, S.; Diehl, M.: Application of a nanoindentation-based approach for parameter identification to a crystal plasticity model for bcc metals. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 881, 145373 (2023)
Li, J.; Pharr, G. M.; Kirchlechner, C.: Quantitative insights into the dislocation source behavior of twin boundaries suggest a new dislocation source mechanism. Journal of Materials Research 36 (10), pp. 2037 - 2046 (2021)
Tian, C.; Dehm, G.; Kirchlechner, C.: Influence of strain rate on the activation of {110}, {112}, {123} slip in ferrite of DP800. Materialia 15, 100983 (2021)
Tian, C.; Kirchlechner, C.: The fracture toughness of martensite islands in dual-phase DP800 steel. Journal of Materials Research 36, pp. 2495 - 2504 (2021)
Ceremony on 16 April with the Minister for Culture and Science of North Rhine-Westphalia, Ina Brandes, the Lord Mayor of Düsseldorf, Stephan Keller, and the President of the Max Planck Society, Patrick Cramer
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