Liu, C.; Roters, F.; Raabe, D.: Finite strain crystal plasticity-phase field modeling of deformation twinning and dislocation slip interaction in hexagonal materials. 18th European Mechanics of Materials Conference, online, Oxford, UK (2022)
Liu, C.; Roters, F.; Raabe, D.: Finite strain crystal plasticity-phase field modeling of twin, dislocation, and grain boundary interactions. 19th International Conference on Strength of Materials ICSMA, Metz, France (2022)
Liu, C.; Shanthraj, P.; Davis, A.; Fellowes, J.; Prangnell, P.; Raabe, D.: Chemo-mechanical phase-field model for two-sublattice phases: phase precipitation in Al–Zn–Mg–Cu alloys. 19th International Conference on Strength of Materials ICSMA, Metz, France (2022)
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)
Liu, C.; Diehl, M.; Shanthraj, P.; Roters, F.; Raabe, D.; Sandlöbes, S.; Dong, J.: An integrated crystal plasticity-phase field approach to locally predict twin formation in magnesium. DGM Meeting, "Herausforderungen bei der skalenübergreifenden Modellierung von Werkstoffen ", Regensburg, Germany (2017)
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.
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…
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.