Schneider, A.; Fu, C.-C.; Waseda, O.; Barreteau, C.; Hickel, T.: Ab initio based models for temperature-dependent magnetochemical interplay in bcc Fe–Mn alloys. Physical Review B 103 (2), 024421 (2021)
Waseda, O.: Machine-Learning assisted Heisenberg model for systems with ill-defined magnetic interactions. Thermec 2018 Conference, Paris, France (2018)
Bekemeier, S.; Rego, C. R. C.; Mai, H. L.; Saikia, U.; Waseda, O.; Apel, M.; Arendt, F.; Aschemann, A.; Bayerlein, B.; Courant, R.et al.; Dziwis, G.; Fuchs, F.; Giese, U.; Junghanns, K.; Kamal, M.; Koschmieder, L.; Leineweber, S.; Luger, M.; Lukas, M.; Maas, J.; Mertens, J.; Mieller, B.; Overmeyer, L.; Pirch, N.; Reimann, J.; Schröck, S.; Schulze, P.; Schuster, J.; Seidel, A.; Shchyglo, O.; Sierka, M.; Silze, F.; Stier, S.; Tegeler, M.; Unger, J. F.; Weber, M.; Hickel, T.; Schaarschmidt, J.: Advancing Digital Transformation in Material Science: The Role of Workflows Within the MaterialDigital Initiative, Advanced Engineering Materials 27, (2025)
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
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.