Hickel, T.; Körmann, F.; Dick, A.; Neugebauer, J.: Fully ab initio based determination of magnetic contributions to the free energy of metals. Psi-k Conference 2010, Berlin, Germany (2010)
Körmann, F.; Dick, A.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Magnetic contributions to the Thermodynamics of iron and Cementite. 448. WE-Heraeus-Seminar "Excitement in magnetism", Ringberg Castle, Tegernsee, Germany (2009)
Körmann, F.; Dick, A.; Grabowski, B.; Hickel, T.; Neugebauer, J.: The free energy of iron: Integrated ab initio derivation of vibrational, electronic, and magnetic contributions. International Workshop on Multiscale Materials Modelling (IWoM3), Berlin, Germany (2009)
Körmann, F.; Dick, A.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Importance of magnetism for the thermal expansion of transition metals: An ab initio study. Spring meeting of the German Physical Society (DPG), Berlin, Germany (2008)
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
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…