Malyar, N.; Dehm, G.; Kirchlechner, C.: Strain rate dependence of the slip transfer through a penetrable high angle grain boundary in copper. Scripta Materialia 138, pp. 88 - 91 (2017)
Malyar, N.; Micha, J.-S.; Dehm, G.; Kirchlechner, C.: Dislocation-twin boundary interaction in small scale Cu bi-crystals loaded in different crystallographic directions. Acta Materialia 129, pp. 91 - 97 (2017)
Dehm, G.; Malyar, N.; Kirchlechner, C.: Towards probing the barrier strength of grain boundaries for dislocation transmission. Electronic Materials and Applications 2017, Orlando, FL, USA (2017)
Dehm, G.; Malyar, N.; Kirchlechner, C.: Do we understand dislocation transmission through grain boundaries? PICS meeting, Luminy, Marseille, France (2017)
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.