Bu, Y.; Li, K.; Ma, Y.; Liang, Z.; Jianliang, Z.; Raabe, D.: Atomistic view of green steel: simulation of early-stage direct reduction of wüstite (FeO) by hydrogen. Chemical Engineering Science 326, 123611 (2026)
Trinca, A.; Verdone, N.; Özgün, Ö.; Ma, Y.; Filho, I.; Raabe, D.; Vilardi, G.: Sustainable ironmaking from low-grade iron ores: A kinetic study on thermal decomposition and reduction of iron (II) oxalate. Journal of Environmental Chemical Engineering 13 (6), 119573 (2025)
Ratzker, B.; Ruffino, M.; Shankar, S.; Raabe, D.; Ma, Y.: Elucidating the microstructure evolution during hydrogen-based direct reduction via a case study of single crystal hematite. Acta Materialia 294, 121174 (2025)
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…