Ma, Y.: Microstructure evolution during hydrogen-based direct reduction of iron oxides. International Workshop on Sustainable Metallurgy of Green Steel (GreenSteel2022), online (2022)
Ma, Y.; Villanova, J.; Requena, G.; Raabe, D.: Understanding the physical-chemical phenomena in green steel production using synchrotron X-ray techniques. European Synchrotron Radiation Facility User Meeting 2022, Online (2022)
Ma, Y.; Zaefferer, S.; Raabe, D.: Hydrogen-based direct reduction of iron ores: Microstructure, crystallography, and reduction mechanisms. 2021 International Metallurgical Processes Workshop for Young Scholars (IMPROWYS2021), a hybrid event, Online (2021)
Ma, Y.: Materials Characterization – Introduction to X-ray Diffraction. Lecture: International Max Planck Research School for Interface Controlled Materials for Energy Conversion (IMPRSURMAT), online, 2021-08
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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.
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…