Diehl, M.; Kusampudi, N.; Kusche, C.; Raabe, D.; Korte-Kerzel, S.: Combining Experiments, Simulations, and Data Science to Understand Damage in Dual Phase Steels. International Conference on Plasticity, Damage, and Fracture, Riviera May, Mexico (2020)
Pei, R.: Microstructural Relationships of Strength and Ductility in a Newly Developed Mg–Al–Zn Alloy for Potential Automotive Applications. Dissertation, RWTH Aachen University (2020)
Pei, R.: Microstructural Relationships of Strength and Ductility in a Newly Developed Mg–Al–Zn Alloy for Po-tential Automotive Applications. Dissertation, RWTH Aachen University (2020)
Chang, Y.: Challenges and opportunities associated to the characterization of H/D in Ti and its alloys with atom probe tomography. Dissertation, RWTH Aachen University (2019)
Choi, W. S.: Deformation mechanisms and the role of interfaces in face-centered cubic Fe-Mn-C micro-pillars. Dissertation, RWTH Aachen, Aachen, Germany (2018)
Morsdorf, L.: Fundamentals of ferrous low-carbon lath martensite: from the as-quenched, to tempered and deformed states. Dissertation, RWTH Aachen, Aachen, 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.
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.
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…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…