Baron, C.; Springer, H.: Property-Driven Development of Metallic Structural Materials by Combinatorial Techniques on the Example of Fe–C–Cr Steels. Steel Research International 90 (12), 1900404 (2019)
Springer, H.; Zhang, J.; Szczepaniak, A.; Belde, M. M.; Gault, B.; Raabe, D.: Light, strong and cost effective: Martensitic steels based on the Fe - Al - C system. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 762, 138088 (2019)
Baron, C.; Springer, H.; Raabe, D.: Development of high modulus steels based on the Fe – Cr – B system. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 724, pp. 142 - 147 (2018)
Aparicio-Fernández, R.; Szczepaniak, A.; Springer, H.; Raabe, D.: Crystallisation of amorphous Fe – Ti – B alloys as a design pathway for nano-structured high modulus steels. Journal of Alloys and Compounds 704, pp. 565 - 573 (2017)
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
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.