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
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
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…