Diehl, M.; Wicke, M.; Shanthraj, P.; Roters, F.; Brueckner-Foit, A.; Raabe, D.: Coupled Crystal Plasticity–Phase Field Fracture Simulation Study on Damage Evolution Around a Void: Pore Shape Versus Crystallographic Orientation. JOM-Journal of the Minerals Metals & Materials Society 69 (5), pp. 872 - 878 (2017)
Koprek, A.; Cojocaru-Mirédin, O.; Würz, R.; Freysoldt, C.; Gault, B.; Raabe, D.: Cd and Impurity Redistribution at the CdS/CIGS Interface After Annealing of CIGS-Based Solar Cells Resolved by Atom Probe Tomography. IEEE Journal of Photovoltaics 7 (1), 7762819, pp. 313 - 321 (2017)
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)
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.
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…