Ma, D.; Friák, M.; Raabe, D.; Neugebauer, J.: Multi-physical alloy approaches to solid solution strengthening of Al. 1st International Conference on Material Modelling, Dortmund, Germany (2009)
Ma, D.; Friák, M.; Raabe, D.; Neugebauer, J.: Investigation of solid solution strengthening by density functional theory. EUROMAT 2009, Glasgow, Scotland, UK (2009)
Ma, D.; Friák, M.; Raabe, D.; Neugebauer, J.: Investigation of solid solution strengthening by density functional theory. 11-th National Congress on Theoretical and Applied Mechanics, Borovets, Bulgaria (2009)
Ma, D.; Friák, M.; Raabe, D.; Neugebauer, J.: Multi-physical alloy approaches to solid solution strengthening of Al. 15th International Conference of Strength of Materials, Dresden, Germany (2009)
Ma, D.; Raabe, D.; Roters, F.; Maaß, R.; van Swygenhoven, H.: Crystal plasticity finite element study on small scale plasticity of micropillars. 15th International Conference on the Strength of Materials (ICSMA-15), Dresden, Germany (2009)
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.