Raabe, D.; Choi, P.-P.; Gault, B.; Ponge, D.; Yao, M.; Herbig, M.: Segregation engineering for self-organized nanostructuring of materials - from atoms to properties? APT&M 2016 - Atom Probe Tomography & Microscopy 2016 (55th IFES) , Gyeongju, South Korea (2016)
Kuzmina, M.; Gault, B.; Herbig, M.; Ponge, D.; Sandlöbes, S.; Raabe, D.: From grains to atoms: ping-pong between experiment and simulation for understanding microstructure mechanisms. Res Metallica Symposium, Department of Materials Engineering, KU Leuven, Leuven, The Netherlands (2016)
Ponge, D.; Herbig, M.; Tasan, C. C.; Raabe, D.: Integrated experimental and simulation analysis of dual phase steels. Workshop on Possibilities and Limitations of Quantitative Materials Modeling and Characterization 2016, Bernkastel, Germany (2016)
Raabe, D.: Materials Engineering through the Ages: from the Battle of Kadesh to Atomic Scale Materials Design. Elite Network of Bavaria (ENB) Forum in Erlangen: Focus on Materials Engineering, Erlangen, Germany (2016)
An, D.; Konijnenberg, P. J.; Zaefferer, S.; Raabe, D.: Correlation between the 5-parametric GBCD and the corrosion resistance of a 304 stainless steel by 3D-EBSD. RMS-EBSD Meeting 2016, Manchester, UK (2016)
Jaya, B. N.; Köhler, M.; Schnabel, V.; Raabe, D.; Schneider, J. M.; Kirchlechner, C.; Dehm, G.: Micro-scale fracture behavior of Co based metallic glass thin films. 2016 TMS Annual Meeting and Exhibition Symposium: In Operando Nano- and Micro-mechanical Characterization of Materials with Special Emphasis on In Situ Techniques, Nashville, TN, USA (2016)
Demura, M.; Raabe, D.; Roters, F.; Hirano, T.: Computational analysis of irregular rolling deformation in Nickel Aluminide single crystals. Thermec 2016, Graz, Austria (2016)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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…