Neugebauer, J.: Materials design and discovery on the computer: Prospects and challenges. Kolloquium Universität Braunschweig , Braunschweig, Germany (2015)
Körmann, F.; Grabowski, B.; Hickel, T.; Neugebauer, J.: Temperature-dependent coupling of atomic and magnetic degree of freedom from first-principles. Electronic Structure Theory for the Accelerated Design of Structural Materials, Moscow, Russia (2015)
Neugebauer, J.: Ab Initio Computation of Phonon-Phonon and Magnon-Phonon Interactions: Successes and Challenges. Workshop DyProSo, Freising, Germany (2015)
Neugebauer, J.: Design of structural materials by predictive ab initio thermodynamics: Challenges, applications and perspectives. Euromat Conference, Warsaw, Poland (2015)
Vatti, A. K.; Todorova, M.; Neugebauer, J.: Formation Energy of Halide ions (Cl/Br/I) in water from ab-initio Molecular Dyna. Psi-k 2015 Conference, San Sebastián, Spain (2015)
Neugebauer, J.: Quantum-mechanical approaches to address the structural and thermodynamic complexity of engineering materials. Swedish Chemical Society, Kalmar, Sweden (2015)
Neugebauer, J.: Understanding the fundamental mechanisms behind H embrittlement: An ab initio guided multiscale approach. Colloquium UCB Vancouver, Vancouver, Canada (2015)
Neugebauer, J.: Vacancies in fcc metals: Discovery of large non-Arrhenius effects. The 5th Sino-German Symposium Thermodynamics and Kinetics of Nano and Mesoscale Materials and Their Applications, Changchun, China (2015)
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
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.
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…