Imrich, P. J.; Kirchlechner, C.; Kiener, D.; Dehm, G.: Internal and external stresses: in situ TEM compression of Cu bicrystals containing a twin boundary. Scripta Materialia 100, pp. 94 - 97 (2015)
Kapp, M. W.; Kapp, M. W.; Kirchlechner, C.; Pippan, R.; Dehm, G.: Importance of dislocations pile-ups on the mechanical properties and the Bauschinger effect in micro cantilevers. Journal of Materials Research 30 (6), pp. 791 - 797 (2015)
Jaya, B. N.; Kirchlechner, C.; Dehm, G.: Can micro-scale fracture tests provide reliable fracture toughness values? A case study in silicon. Journal of Materials Research 30 (5), pp. 686 - 698 (2015)
Heinz, W.; Robl, W.; Dehm, G.: Influence of initial microstructure on thermomechanical fatigue behavior of Cu films on substrates. Microelectronic Engineering 137, pp. 5 - 10 (2015)
Zhang, Z.; Dehm, G.: Study on the Atomic and Electronic Structure in CrN (VN, TiN) Films using Cs-Corrected TEM. Microscopy and Microanalysis 21 (3), pp. 2079 - 2080 (2015)
Rashkova, B.; Faller, M.; Pippan, R.; Dehm, G.: Growth mechanism of Al2Cu precipitates during in situ TEM heating of a HPT deformed Al–3wt.%Cu alloy. Journal of Alloys and Compounds 600, pp. 43 - 50 (2014)
Imrich, P. J.; Kirchlechner, C.; Motz, C.; Dehm, G.: Differences in deformation behavior of bicrystalline Cu micropillars containing a twin boundary or a large-angle grain boundary. Acta Materialia 73, pp. 240 - 250 (2014)
Harzer, T. P.; Daniel, R.; Mitterer, C.; Dehm, G.; Zhang, Z. L.: Transmission electron microscopy characterization of CrN films on MgO(001). Thin Solid Films 545, pp. 154 - 160 (2013)
Daum, B.; Dehm, G.; Clemens, H.; Rester, M.; Fischer, F. D.; Rammerstorfer, F. G.: Elastoplastic buckling as source of misinterpretation of micropillar tests. Acta Materialia 61 (13), pp. 4996 - 5007 (2013)
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
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…