Calcagnotto, M.; Ponge, D.; Raabe, D.: Fabrication of ultrafine grained dual phase steels. Lecture: National Institute for Materials Science (NIMS), Tsukuba, Japan, October 22, 2007
Raabe, D.: Modellierung und Simulation materialwissenschaftlicher Prozesse. Lecture: Symposion: Digitale Modellierung, Simulation und Visualisierung, Akademie der Wissenschaften und der Literatur, Mainz, March 09, 2007
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
In this project, links are being established between local chemical variation and the mechanical response of laser-processed metallic alloys and advanced materials.
Advanced microscopy and spectroscopy offer unique opportunities to study the structure, composition, and bonding state of individual atoms from within complex, engineering materials. Such information can be collected at a spatial resolution of as small as 0.1 nm with the help of aberration correction.
The goal of this project is to develop an environmental chamber for mechanical testing setups, which will enable mechanical metrology of different microarchitectures such as micropillars and microlattices, as a function of temperature, humidity and gaseous environment.
The prediction of materials properties with ab initio based methods is a highly successful strategy in materials science. While the working horse density functional theory (DFT) was originally designed to describe the performance of materials in the ground state, the extension of these methods to finite temperatures has seen remarkable…