Frommeyer, G.; Knippscheer, S.; Rablbauer, R.: Struktur und Eigenschaften von Titanaluminiden (TiAl) - Leichtbaulegierungen für High Performance Motorkomponenten. Clauthal Industriekolloquium Sonderforschungsbereich 675, Clausthal (2007)
Frommeyer, G.; Rablbauer, R.; Brokmeier, K.: Entwicklung und Eigenschaften ultrahochfester und supraduktiler Stähle für den Fahrzeugbau. Clausthal Industriekolloquium Sonderforschungsbereich 675, Clausthal (2007)
Rablbauer, R.; Dönecke, K.; Hassel, A. W.; Frommeyer, G.: Mechanical Properties and Corrosion Behaviour of Ferritic Stainless Al Cr Steels. EUROMAT 2007, European Congress and Exhibition an Advanced Materials and Processes, Nürnberg, Germany (2007)
Hassel, A. W.; Lill, K. A.; Rablbauer, R.; Stratmann, M.: Corrosion and passivity of FeAlCr light weight steels. 58th Annual Meeting of the International Society of Electrochemistry, Banff, Canada (2007)
Frommeyer, G.; Rablbauer, R.; Fischer, R.: Properties of refractory NiAl(Cr, Mo, Re) alloys in relation to atomic defects and microstructures. TMS 2007 Annual Meeting, Orlando, FL, USA (2007)
Frommeyer, G.; Rablbauer, R.: Properties of refractory NiAl-(Cr, Mo, Re) alloys in relation to Atomic Defects and Microstructures. High Temperature Materials Chemistry, Wien, Austria (2006)
Rablbauer, R.: Mikrostrukturen und Eigenschaften quasibinärer eutektischer NiAl-Re und NiAl-(Ti,Zr,Hf)B2-Legierungen für den Hochtemperatureinsatz. Dissertation, RWTH Aachen, Aachen, Germany (2006)
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
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…