Rao, Z.; Bajpai, A.; Zhang, H.: Active learning strategies for the design of sustainable alloys. Philosophical Transactions of the Royal Society A 382 (2284), 20230242 (2024)
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)
Pyczak, F.; Liang, Z.; Neumeier, S.; Rao, Z.: Stability and Physical Properties of the L12-γ' Phase in the CoNiAlTi-System. Metallurgical and Materials Transactions A 54 (5), pp. 1661 - 1670 (2023)
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)
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
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…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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.