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)
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)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…