Jentner, R.; Scholl, S.; Srivastava, K.; Best, J. P.; Kirchlechner, C.; Dehm, G.: Local strength of bainitic and ferritic HSLA steel constituents understood using correlative electron microscopy and microcompression testing. Materials and Design 236, 112507 (2023)
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 (18), pp. 4177 - 4191 (2023)
Jentner, R.; Best, J. P.; Kirchlechner, C.; Dehm, G.: Challenges in the phase identification of steels using unsupervised clustering of nanoindentation data. Nanomechanical Testing in Materials Research and Development VIII, Split, Croatia (2022)
Jentner, R.: Phase identification and micromechanical characterization of an advanced high-strength low-alloy steel. Dissertation, Ruhr-Universität Bochum (2023)
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
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.