Herbig, M.; Kumar, A.: Removal of hydrocarbon contamination and oxide films from atom probe specimens. Microscopy Research and Technique 84 (2), pp. 291 - 297 (2021)
Kumar, A.; Dutta, A.; Makineni, S. K.; Herbig, M.; Petrov, R.; Sietsma, J.: In-situ observation of strain partitioning and damage development in continuously cooled carbide-free bainitic steels using micro digital image correlation. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 757, pp. 107 - 116 (2019)
Kwiatkowski da Silva, A.; Inden, G.; Kumar, A.; Ponge, D.; Gault, B.; Raabe, D.: Competition between formation of carbides and reversed austenite during tempering of a medium-manganese steel studied by thermodynamic-kinetic simulations and atom probe tomography. Acta Materialia 147, pp. 165 - 175 (2018)
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
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…