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)
Wang, N.; Freysoldt, C.; Zhang, S.; Liebscher, C.; Neugebauer, J.: Segmentation of Static and Dynamic Atomic-Resolution Microscopy Data Sets with Unsupervised Machine Learning Using Local Symmetry Descriptors. Microscopy and Microanalysis 27 (6), pp. 1454 - 1464 (2021)
Devulapalli, V.; Bishara, H.; Ghidelli, M.; Dehm, G.; Liebscher, C.: Influence of substrates and e-beam evaporation parameters on the microstructure of nanocrystalline and epitaxially grown Ti thin films. Applied Surface Science 562, 150194 (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
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…
Crystal Plasticity (CP) modeling [1] is a powerful and well established computational materials science tool to investigate mechanical structure–property relations in crystalline materials. It has been successfully applied to study diverse micromechanical phenomena ranging from strain hardening in single crystals to texture evolution in…