Baron, C.; Springer, H.; Raabe, D.: Efficient liquid metallurgy synthesis of Fe–TiB2 high modulus steels via in-situ reduction of titanium oxides. Materials and Design 97, pp. 357 - 363 (2016)
Tarzimoghadam, Z.; Rohwerder, M.; Merzlikin, S. V.; Bashir, A.; Yedra , L.; Eswara, S.; Ponge, D.; Raabe, D.: Multi-scale and spatially resolved hydrogen mapping in a Ni–Nb model alloy reveals the role of the δ phase in hydrogen embrittlement of alloy 718. Acta Materialia 109, pp. 69 - 81 (2016)
Cereceda, D.; Diehl, M.; Roters, F.; Raabe, D.; Perlado, J. M.; Marian, J.: Unraveling the temperature dependence of the yield strength in single-crystal tungsten using atomistically-informed crystal plasticity calcula- tions. International Journal of Plasticity 78, pp. 242 - 265 (2016)
Springer, H.; Belde, M. M.; Raabe, D.: Combinatorial design of transitory constitution steels: Coupling high strength with inherent formability and weldability through sequenced austenite stability. Materials and Design 90, pp. 1100 - 1109 (2016)
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.