Sandim, H. R. Z.; Sandim, M. J. R.; Bernardi, H. H.; Lins, J. F. C.; Raabe, D.: Annealing effects on the microstructure and texture of a multifilamentary Cu–Nb composite wire. Scripta Materialia 51, pp. 1099 - 1104 (2004)
Lima, E. B. F.; Pyzalla, A. R.; Reimers, W.; Kuo, J.-C.; Raabe, D.: Mosaic Size Distributions in an Aluminum Bi-crystal Deformed by Channel Die Plane Strain Compression. Journal of Neutron Research 11 (4), pp. 209 - 214 (2003)
Zaefferer, S.; Kuo, J. C.; Zhao, Z.; Winning, M.; Raabe, D.: On the influence of the grain boundary misorientation on the plastic deformation of aluminum bicrystals. Acta Materialia 51, pp. 4719 - 4735 (2003)
Raabe, D.: Don’t trust your simulation - Computational materials science on its way to maturity? Advanced Engineering Materials 4 (5), pp. 255 - 267 (2002)
Raabe, D.; Zhao, Z.; Park, S. J.; Roters, F.: Theory of orientation gradients in plastically strained crystals. Acta Materialia 50 (2), pp. 421 - 440 (2002)
Park, S. J.; Han, H. N.; Oh, K. H.; Raabe, D.; Kim, J. K.: Finite element simulation of grain interaction and orientation fragmentation during plastic deformation of BCC metals. Proc. ICOTOM 13, pp. 371 - 376 (2002)
Raabe, D.: Cellular automata in materials science with particular reference to recrystallization simulation. Annual Review of Materials Research 32, pp. 53 - 76 (2002)
Raabe, D.; Roters, F.; Zhao, Z.: Texture component crystal plasticity finite element method for physically-based metal forming simulations including texture update. Proc. 8th Int. Conf. on Aluminium Alloys, pp. 31 - 36 (2002)
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.
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…