Zhou, X.; Wei, S.; Raabe, D.: Segregation-Driven Mechanics of White Gold at the Nanoscale: A Cursing or Blessing? Schöntal Symposium on Dislocation-based Plasticity 2024, Kloster Schöntal, Germany (2024)
Umate, K. S.; Bai, Y.; Svendsen, B.; Raabe, D.: Phase-field model for Hydrogen based direct reduction of iron oxides: Role of porosity. TMS - Algorithm Development in Materials Science and Engineering, Orlando, FL, USA (2024)
Raabe, D.: Transport and phase transformations phenomena in sustainable hydrogen-based steel production. 87th Spring Meeting of the German Physical Society, Berlin, Germany (2024)
Feng, S.; Gong, Y.; Neugebauer, J.; Raabe, D.; Liotti, E.; Grant, P. S.: Multi-technique investigation of Fe-rich intermetallic compounds for more impurity-tolerant Al alloys. Annual Meeting of DPG and DPG-Frühjahrstagung (DPG Spring Meeting) of the Condensed Matter Section (SKM) 2024, Berlin, Germany (2024)
Raabe, D.: Basic Materials Science Aspects of Green Metal Production. Royal Society Conference on Sustainable Metals: Science and Systems, London, UK (2024)
Raabe, D.: The Interplay of Lattice Defects and Chemistry at Atomic Scale and Why it Matters for the Properties of Materials. Van Horn Distinguished Lecturer Series, Cleveland, OH, USA (2023)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
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.