Tillack, N.; Hickel, T.; Raabe, D.; Neugebauer, J.: Kinetic Monte Carlo simulations and ab initio studies of nano-precipitation in ferritic steels. Computational Materials Science on Complex Energy Landscapes Workshop, Imst, Austria (2010)
Tillack, N.; Yates, J. R.; Roberts, S. G.; Hickel, T.; Drautz, R.; Neugebauer, J.: First-Principles Investigations of ODS Steels. Ab initio Description of Iron and Steel: Thermodynamics and Kinetics, Tegernsee, Germany (2012)
Tillack, N.; Hickel, T.; Raabe, D.; Neugebauer, J.: Ab initio study of nano-precipitate nucleation and growth in ferritic steels. Psi-k/CECAM/CCP9 Biennial Graduate School in Electronic-Structure Methods, Oxford, UK (2011)
Tillack, N.; Hickel, T.; Raabe, D.; Neugebauer, J.: Ab initio study of nano-precipitate nucleation and growth in ferritic steels. Materials Discovery by Scale-Bridging High-Throughput Experimentation and Modelling, Ruhr-Universität Bochum, Bochum, Germany (2010)
Tillack, N.; Hickel, T.; Raabe, D.; Neugebauer, J.: Ab initio and kinetic Monte-Carlo study of nano-precipitate nucleation and growth in ferritic steels. Materials Discovery by Scale-Bridging High-Throughput Experimentation and Modelling, Bochum, Germany (2010)
Tillack, N.; Hickel, T.; Raabe, D.; Neugebauer, J.: Kinetic Monte Carlo and ab initio study of nano-precipitates and growth in ferritic steels. Ab Initio Description of Iron and Steel: Mechanical Properties, Tegernsee, Germany (2010)
Tillack, N.; Hickel, T.; Raabe, D.; Neugebauer, J.: Combined ab initio studies and kinetic Monte Carlo simulations of nano-precipitation in ferritic steels. Summer School: Computational Materials Science, San Sebastian, Spain (2010)
Tillack, N.: Chemical Trends in the Yttrium-Oxide Precipitates in Oxide Dispersion Strengthened Steels: A First-Principles Investigation. Master, Ruhr-Universität Bochum, Bochum, Germany (2012)
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
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.