Aydin, U.; Hickel, T.; Neugebauer, J.: Combining ab initio with data mining techniques: Solution enthalpy of hydrogen in transition metals. DPG Frühjahrstagung 2012, Berlin, Germany (2012)
Aydin, U.; Hickel, T.; Neugebauer, J.: High-Throughput Computation: The solution enthalpy of hydrogen in 3d metals derived from first principles. International workshop on Materials Discovery by Scale-Bridging High-Throughput, Bochum, Germany (2010)
Aydin, U.; Hickel, T.; Neugebauer, J.: The solution enthalpy of hydrogen derived from first principles along the series of 3d metals. Ab initio description of Iron and Steel: Mechanical Properties, 468. Wilhelm und Else Heraeus-Seminar, Ringberg, Germany (2010)
Aydin, U.; Ismer, L.; Hickel, T.; Neugebauer, J.: Chemical trends of the solution enthalpy of dilute hydrogen in 3d transition metals, derived from first principles. Summer School: Computational Materials Science, San Sebastian, Spain (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
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…
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.