Hickel, T.: Understanding complex materials at finite temperatures by ab inito methods. Colloquium at Institut für Materialwissenschaft, Universtität Stuttgart, Stuttgart, Germany (2014)
Hickel, T.: Ab initio basierte Methoden der mechanismen-orientierten Werkstoffentwicklung. Colloquium at Salzgitter-Mannesmann-Forschung GmbH, Duisburg, Germany (2014)
Hickel, T.; Nazarov, R.; McEniry, E.; Dey, P.; Neugebauer, J.: Impact of light elements on interface properties in steels. CECAM workshop “Modeling Metal Failure Across Multiple Scales”, Lausanne, Switzerland (2014)
Hickel, T.: Understanding complex materials at finite temperatures by ab inito methods. Physikalisches Kolloquium der TU Chemnitz, Chemnitz, Germany (2014)
Hickel, T.; Körmann, F.; Bleskov, I.; Neugebauer, J.: Ab Initio Based Modelling of Stacking Fault Energies in High-Strength Steels. International Seminar on Process Chain Simulation and Related Topics, Karlsruhe, Germany (2014)
Bleskov, I.; Hickel, T.; Neugebauer, J.: Impact of Local Magnetism on Stacking Fault Energies: A First Principles Investigation for fcc Iron. Condensed Matter - Université Paris Descartes, Paris, France (2014)
Bleskov, I.; Hickel, T.; Neugebauer, J.: Impact of Local Magnetism on Stacking Fault Energies: A First Principles Investigation for fcc Iron. TMS 2014, San Diego, CA, USA (2014)
Dey, P.; Nazarov, R.; Hickel, T.; Neugebauer, J.: Ab-initio study of hydrogen trapping by kappa-carbides in an austenitic Fe matrix. DPG Frühjahrstagung, Dresden, Germany (2014)
Dutta, B.; Hickel, T.; Neugebauer, J.: Coupling of lattice dynamics and magnetism in magnetic shape memory alloys: Consequences for phase diagrams. Asia Sweden meeting on understanding functional materials from lattice dynamics (ASMFLD) conference, Indian Institute of technology Guwahati, Guwahati, India (2014)
Hickel, T.; Glensk, A.; Grabowski, B.; Körmann, F.; Neugebauer, J.: Thermodynamics of materials up to the melting point: The role of anharmonicities. Asia Sweden Meeting on Understanding Functional Materials from Lattice dynamics, Guwahati, India (2014)
Körmann, F.; Hickel, T.; Neugebauer, J.: Phase stabilities of metals and steels - The impact of magnetic excitations from fi rst-principles. ADIS (Ab initio Description of Iron and Steel) Conference 2014 , Ringberg Castle, Rottach-Egern, Germany (2014)
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
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…
Ever since the discovery of electricity, chemical reactions occurring at the interface between a solid electrode and an aqueous solution have aroused great scientific interest, not least by the opportunity to influence and control the reactions by applying a voltage across the interface. Our current textbook knowledge is mostly based on mesoscopic…
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.