Rao, Z.; Li, Y.; Zhang, H.; Colnaghi, T.; Marek, A.; Rampp, M.; Gault, B.: Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms. Scripta Materialia 234, 115542 (2023)
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…
The goal of this project is the investigation of interplay between the atomic-scale chemistry and the strain rate in affecting the deformation response of Zr-based BMGs. Of special interest are the shear transformation zone nucleation in the elastic regime and the shear band propagation in the plastic regime of BMGs.
Developing and providing accurate simulation techniques to explore and predict structural properties and chemical reactions at electrified surfaces and interfaces is critical to surmount materials-related challenges in the context of sustainability, energy conversion and storage. The groups of C. Freysoldt, M. Todorova and S. Wippermann develop…
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…