Han, F.; Diehl, M.; Roters, F.; Raabe, D.: Multi-scale modeling of plasticity. ICIAM 2019 - The 9th International Congress on Industrial and Applied Mathematics, Valencia, Spain (2019)
Liu, C.; Shanthraj, P.; Roters, F.; Raabe, D.: Phase-field/CALPHAD methods for multi-phase and multi-component microstructures. The 4th International Symposium on Phase Field Modelling in Materials Science (PF 19), Bochum, Germany (2019)
Raabe, D.: Metastable Nanostructured Metallic Alloy. The KAIST Lecture in Materials Science and Engineering 2019, Korea Advanced Institute of Science and Technology KAIST, Daejeon, Korea (2019)
Raabe, D.: Atomic-Scale Analysis of Chemistry at Lattice Defects. The KAIST Lecture in Materials Science and Engineering 2019, Korea Advanced Institute of Science and Technology KAIST, Daejeon, Korea (2019)
Su, J.; Raabe, D.; Li, Z.: On the mechanism of displacive phase transformation in metastable high entropy alloys. DPG Regensburg 2019, Regensburg, Germany (2019)
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
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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…