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)
Özgün, Ö.; Dirba, I.; Gutfleisch, O.; Ma, Y.; Raabe, D.: Green Ironmaking at Higher H2 Pressure: Reduction Kinetics and Microstructure Formation During Hydrogen-Based Direct Reduction of Hematite Pellets. Journal of Sustainable Metallurgy 10, pp. 1127 - 1140 (2024)
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
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.
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…