Neugebauer, J.: Utilizing solid-solid phase transitions in the design of novel steels: An ab initio approach. PTM2010 Solid-Solid Phase Transformations in Inorganic Materials, Avignon, France (2010)
Grabowski, B.; Ismer, L.; Hickel, T.; Neugebauer, J.: Ab initio concepts for an efficient and accurate determination of thermodynamic properties up to the melting point. Calphad XXXIX, Jeju Island, South Korea (2010)
Körmann, F.; Dick, A.; Hickel, T.; Neugebauer, J.: First principles concepts to determine the heat capacity of Fe-based alloys. Calphad XXXIX, Jeju Island, South Korea (2010)
Udyansky, A.; von Pezold, J.; Dick, A.; Neugebauer, J.: Impurity ordering in iron: An ab initio based multi-scale approach. GraCoS Workshop (Carbon and Nitrogen in Steels: Measurement, Phase Transformations and Mechanical Properties), Rouen, France (2010)
Abu-Farsakh, H.; Neugebauer, J.: Exploring the unusual diffusion of N adatoms on GaAs(001) using first principles calculations. DPG Frühjahrstagung 2010, Regensburg, Germany (2010)
Aydin, U.; Ismer, L.; Hickel, T.; Neugebauer, J.: Chemical trends of the solution enthalpy of hydrogen in 3d transition metals in dilute limit, derived from first principles. DPG Frühjahrstagung 2010, Regensburg, Germany (2010)
Kim, O.; Friák, M.; Neugebauer, J.: Ab initio analysis of the carbon solubility limits in various iron allotropes. DPG Frühjahrstagung 2010, Regensburg, Germany (2010)
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…