Vasileiadis, I. G.; Lymperakis, L.; Adikimenakis, A.; Gkotinakos, A.; Devulapalli, V.; Liebscher, C.; Androulidaki, M.; Hübner, R.; Georgakilas, A. I.; Karakostas, T.et al.; Komninou, P.; Dimakis, E.; Dimitrakopulos, G. P.: Unraveling the composition of monolayer-thick InGaN/GaN quantum wells: A quantitative analysis via probe-corrected HRSTEM. BIO Web of Conferences 129, 24024 (2024)
Torres, P. A. L.; Li, Y.-S.; Grön, C.; Lazaridis, T.; Watermeyer, P.; Cheng, N.; Liebscher, C.; Gasteiger, H. A.: ORR Activity and Voltage-Cycling Stability of a Carbon-Supported PtxY Alloy Catalyst Evaluated in a PEM Fuel Cell. Journal of the Electrochemical Society 170 (12), 124503 (2023)
Leitherer, A.; Yeo, B. C.; Liebscher, C.; Ghiringhelli, L. M.: Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy. npj Computational Materials 9 (1), 179 (2023)
Harrison, P.; Zhou, X.; Das, S. M.; Lhuissier, P.; Liebscher, C.; Herbig, M.; Ludwig, W.; Rauch, E. F.: Reconstructing dual-phase nanometer scale grains within a pearlitic steel tip in 3D through 4D-scanning precession electron diffraction tomography and automated crystal orientation mapping. Ultramicroscopy 238, 113536 (2022)
Zhu, Z.; Ng, F. L.; Seet, H. L.; Lu, W.; Liebscher, C.; Rao, Z.; Raabe, D.; Nai, S. M. L.: Superior mechanical properties of a selective-laser-melted AlZnMgCuScZr alloy enabled by a tunable hierarchical microstructure and dual-nanoprecipitation. Materials Today 52, pp. 90 - 101 (2022)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
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
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…