Wong, S. L.; Laptyeva, G.; Brüggemann, T.; Karhausen, K.-F.; Roters, F.; Raabe, D.: An improved unified internal state variable model exploiting first principle calculations for flow stress modeling of aluminium alloys. International Conference on Aluminum Alloys (ICAA), Montreal, Canada (2018)
Niendorf, T.; Wegener, T.; Li, Z.; Raabe, D.: On the fatigue behavior of dual-phase high-entropy alloys in the low-cycle fatigue regime. Fatique 2018, Poitiers, France (2018)
Kontis, P.; Raabe, D.; Gault, B.: The role of systematic characterization on the development of new nickel-based superalloys. Industrial Colloquium - SFB/TR 103 „From Atoms to Turbine Blades“ , Fürth, Germany (2018)
Kürnsteiner, P.; Wilms, M. B.; Weisheit, A.; Jägle, E. A.; Raabe, D.: Preventing the Coarsening of Al3Sc Precipitates by the Formation of a Zr-rich Shell During Laser Metal Deposition. TMS2018 Annual Meeting & Exhibition, Phoenix, AZ, USA (2018)
Kwiatkowski da Silva, A.; Inden, G.; Ponge, D.; Gault, B.; Raabe, D.: Precipitation of CFCC-TmC Carbides during Tempering at 450°C of a Medium Mn Steel: A Thermodynamic and Kinetic Study Followed by Atom Probe Tomography. TMS 2018 Annual Meeting & Exhibition, Phoenix, AZ, USA (2018)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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 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…