Pemma, S.; Janisch, R.; Dehm, G.; Brink, T.: Atomistic simulation study of grain boundary migration for different complexions in copper. DPG-Tagung, Virtual (2021)
Brognara, A.; Best, J. P.; Djemia, P.; Faurie, D.; Dehm, G.; Ghidelli, M.: Toward engineered thin film metallic glasses with large mechanical properties: effect of composition and nanostructure. Seminar at Laboratoire des Sciences des Procédés et des Matériaux (LSPM), Paris Nord University, Paris, France (2021)
Brink, T.; Frommeyer, L.; Freitas, R.; Frolov, T.; Pemma, S.; Liebscher, C.; Dehm, G.: Diffusionless congruent grain boundary phase transitions in metals: Simulation and experimental imaging. 2021 Fall Meeting of the European Materials Research
Society
, Virtual (2021)
Tsybenko, H.; Dehm, G.; Brinckmann, S.: Deformation and chemical evolution in cementite (Fe3C) during small-scale tribology. European Congress and Exhibition on Advanced Materials and Processes - EUROMAT 2021, Virtual (2021)
Frommeyer, L.; Brink, T.; Freitas, R.; Frolov, T.; Dehm, G.; Liebscher, C.: Congruent grain boundary phase transformations revealed by STEM in pure copper. Microscopy conference Joint Meeting of Dreiländertagungn & Multinational Congress on Microscopy MC 2021, virtual, Vienna, Austria (2021)
Dehm, G.: Experimental Insights in Congruent and Non-Congruent Grain Boundary Phase Transformations in Copper by Advanced STEM. International Seminars, Technion - Israel Institute of Technology (Israel), Purdue University (USA), virtual (2021)
Dehm, G.: Congruent and non-congruent grain boundary phase transformations in Copper studied by advanced STEM. Virtual Seminar of Institute Jozef Stefan, Ljubljana, Slovenia (2021)
Liebscher, C.; Lu, W.; Dehm, G.; Raabe, D.; Li, Z.: Complex phase transformation pathways in high entropy alloys explored by in situ S/TEM. Third International Conference on High Entropy Materials, Berlin, Germany (2020)
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.