Gault, B.: Full determination of 3D atomic position by combining APT & EM. Scientific Directions for Future TEM, Forschungszentrum Jülich, Jülich, Germany (2016)
Gault, B.; Katnagallu, S.: Atom probe microscopy: a new playground for big data analysis? Workshop Big-Data-Driven Materials Science, Ringberg Castle, Rottach, Germany (2016)
Gault, B.; De Geuser, F.: A perspective on the ion projection in field ion & atom probe microscopy. Atom Probe Tomography & Microscopy 2016, Gyeongju, South Korea (2016)
Raabe, D.; Choi, P.-P.; Gault, B.; Ponge, D.; Yao, M.; Herbig, M.: Segregation engineering for self-organized nanostructuring of materials - from atoms to properties? APT&M 2016 - Atom Probe Tomography & Microscopy 2016 (55th IFES) , Gyeongju, South Korea (2016)
Kuzmina, M.; Gault, B.; Herbig, M.; Ponge, D.; Sandlöbes, S.; Raabe, D.: From grains to atoms: ping-pong between experiment and simulation for understanding microstructure mechanisms. Res Metallica Symposium, Department of Materials Engineering, KU Leuven, Leuven, The Netherlands (2016)
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.
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.