Springer, H.; Raabe, D.; Belde, M. M.: Rapid Alloy Prototyping – High Throughput Bulk Metallurgy at the MPIE. Workshop on machine learning and data analytics in advanced metals processing, RollsRoyce Institute Manchester, Manchester, UK (2017)
Springer, H.; Belde, M. M.; Raabe, D.: High throughput combinatorial design of novel high performance steels. International conference on High-throughput materials development, Ghent, Belgium (2015)
Belde, M. M.; Springer, H.; Raabe, D.: Tailoring multi-phase microstructures by control of local chemical gradients, applied to austenite in martensitic steel. Icomat 2014
, Bilbao, Spain (2014)
Zhang, J.; Tasan, C. C.; Lai, M.; Springer, H.; Raabe, D.: Influence of oxygen and cold deformation on the ω phase formation in gum metal. TMS 2014, San Diego, TX, USA (2014)
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.