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
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…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
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.
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…