Neugebauer, J.: Efficient sampling of high-dimensional chemical and thermodynamic configuration spaces. ELRC2020: Invitation to Complex High-Dimensional Energy Landscapes Reunion Conference II, Delivered online, Lake Arrowhead, CA, USA (2021)
Neugebauer, J.; Zendegani, A.; Hickel, T.: Defect phase diagrams as novel tool to understand and design tailored defect structures in advanced steels. Thermec2021, Virtual Meeting, Vienna, Austria (2021)
Todorova, M.; Surendralal, S.; Wippermann, S. M.; Deißenbeck, F.; Neugebauer, J.: Insights into processes at electrochemical solid/liquid interfaces from ab initio molecular dynamics simulations. ICTP-Workshop on “Physics and Chemistry of Solid/Liquid Interfaces for Energy Conversion and Storage”, Virtual Meeting, Trieste, Italy (2021)
Neugebauer, J.: Materials design by exploiting high-dimensional chemical and structural configuration spaces. Kolloquium im Rahmen des SFB 986, Technische Universität Hamburg-Harburg, Online Meeting, Hamburg-Harburg, Germany (2021)
Janßen, J.; Hickel, T.; Neugebauer, J.: pyiron – an integrated development environment for ab initio thermodynamics. Potential Workshop, ICAMS, virtual, Bochum, Germany (2021)
Neugebauer, J.; Ikeda, Y.; Körmann, F.: Materials design based on efficient sampling of high dimensional chemical and thermodynamic configuration spaces. Workflows for Atomistic Simulations, Ruhr-Universität Bochum, Online Meeting, Bochum, Germany (2021)
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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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.