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)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
Many important phenomena occurring in polycrystalline materials under large plastic strain, like microstructure, deformation localization and in-grain texture evolution can be predicted by high-resolution modeling of crystals. Unfortunately, the simulation mesh gets distorted during the deformation because of the heterogeneity of the plastic…
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.
The project HyWay aims to promote the design of advanced materials that maintain outstanding mechanical properties while mitigating the impact of hydrogen by developing flexible, efficient tools for multiscale material modelling and characterization. These efficient material assessment suites integrate data-driven approaches, advanced…
A novel design with independent tip and sample heating is developed to characterize materials at high temperatures. This design is realized by modifying a displacement controlled room temperature micro straining rig with addition of two miniature hot stages.
The structures of grain boundaries (GBs) have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the near-atomic scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability…
Hydrogen embrittlement is one of the most substantial issues as we strive for a greener future by transitioning to a hydrogen-based economy. The mechanisms behind material degradation caused by hydrogen embrittlement are poorly understood owing to the elusive nature of hydrogen. Therefore, in the project "In situ Hydrogen Platform for…