Raabe, D.: Compositional Lattice Defect Manipulation for Microstructure Design. The Bauerman Lecture 2019, Department of Materials, Imperial College London, Royal School of Mines, London, UK (2019)
Sedighiani, K.; Diehl, M.; Roters, F.; Sietsma, J.; Raabe, D.: Obtaining constitutive parameters for a physics-based crystal plasticity model from macro-scale behavior. International Conference on Plasticity, Damage, and Fracture , Panama City, Panama (2019)
Li, Z.; Su, J.; Lu, W.; Wang, Z.; Raabe, D.: Metastable high-entropy alloys: design, structure and properties. 2nd International Conference on High-Entropy Materials (ICHEM 2018), Jeju, South Korea (2018)
Seol, J. B.; Ko, W.-S.; Bae, J. W.; Jo, Y. H.; Li, Z.; Choi, P.-P.; Raabe, D.; Kim, H. S.: Transition in boron boundary cohesion from effectiveness to harmfulness with respect to application temperatures: high-entropy alloys and Ni-based superalloys. 2nd International Conference on High-Entropy Materials (ICHEM 2018), Jeju, South Korea (2018)
Lu, W.; Li, Z.; Liebscher, C.; Dehm, G.; Raabe, D.: TEM/STEM Investigations of the TRIP Effect in a Dual-Phase High-Entropy Alloy. MRS Fall Meeting, Boston, MA, USA (2018)
Su, J.; Li, Z.; Raabe, D.: Microstructural Design to Improve the Mechanical Properties of an Interstitial TRIP-TWIP High-Entropy Alloy. MRS Fall Meeting , Boston, MA, USA (2018)
Sun, B.; Ponge, D.; Fazeli, F.; Scott, C.; Yue, S.; Raabe, D.: Revealing fracture mechanisms of medium manganese steels with and without delta-ferrite. 6th International Conference on Advanced Steels (ICAS 2018), Jeju, South Korea (2018)
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…