Stein, F.; Leineweber, A.: Laves phases: a review of their functional and structural applications and an improved fundamental understanding of stability and properties. Journal of Materials Science 56, pp. 5321 - 5427 (2021)
Fonović, M.; Leineweber, A.; Robach, O.; Jägle, E. A.; Mittemeijer, E. J.: The Nature and Origin of ‘‘Double Expanded Austenite’’ in Ni-Based Ni–Ti Alloys Developing Upon Low Temperature Gaseous Nitriding. Metallurgical and Materials Transactions a-Physical Metallurgy and Materials Science 46 (9), pp. 4115 - 4131 (2015)
Leineweber, A.; Berger, T.; Udyansky, A.; Bugaev, V. N.; Duppel, V.: The incommensurate crystal structure of the Pd5b1-z phase; B ordering driven by elastic interaction between B atoms. Zeitschrift für Kristallographie: International Journal for Structural, Physical, and Chemical Aspects of Crystalline Materials 229 (5), pp. 353 - 367 (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
Hydrogen in aluminium can cause embrittlement and critical failure. However, the behaviour of hydrogen in aluminium was not yet understood. Scientists at the Max-Planck-Institut für Eisenforschung were able to locate hydrogen inside aluminium’s microstructure and designed strategies to trap the hydrogen atoms inside the microstructure. This can…
Thermo-chemo-mechanical interactions due to thermally activated and/or mechanically induced processes govern the constitutive behaviour of metallic alloys during production and in service. Understanding these mechanisms and their influence on the material behaviour is of very high relevance for designing new alloys and corresponding…
Electron channelling contrast imaging (ECCI) is a powerful technique for observation of extended crystal lattice defects (e.g. dislocations, stacking faults) with almost transmission electron microscopy (TEM) like appearance but on bulk samples in the scanning electron microscope (SEM).
The project aims to study corrosion, a detrimental process with an enormous impact on global economy, by combining denstiy-functional theory calculations with thermodynamic concepts.
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.