Philippi, B.; Kirchlechner, C.; Micha, J.-S.; Dehm, G.: Size and orientation dependent mechanical behavior of body-centered tetragonal Sn at 0.6 of the melting temperature. Acta Materialia 115, pp. 76 - 82 (2016)
Schüler, K.; Philippi, B.; Weinmann, M.; Marx, V. M.; Vehoff, H.: Effects of processing on texture, internal stresses and mechanical properties during the pulsed electrodeposition of nanocrystalline and ultrafine-grained nickel. Acta Materialia 61 (11), pp. 3945 - 3955 (2013)
Philippi, B.; Kirchlechner, C.; Schießl, A.; Schingale, A.; Dehm, G.: Improving lead-free solders by resolving mechanical properties at the microstructure length scale. Thin Film & Small Scale Mechanical Behavior 2014, Gordon Research Conference, Waltham, MA, USA (2014)
Philippi, B.; Schießl, A.; Schingale, A.; Dehm, G.: Micromechanical investigation of solder joints in automotive microelectronics. GDRi CNRS MECANO General Meeting on the Mechanics of Nano-Objects, MPIE, Düsseldorf, Germany (2013)
Philippi, B.; Schießl, A.; Schingale, A.; Dehm, G.: Micromechanical investigation of solder joints for automotive microelectronics. Nano- and Micromechanical Testing in Materials Research and Development IV, Olhão Algarve, Portugal (2013)
Philippi, B.: Micromechanical characterization of lead-free solder and its individual microstructure elements. Dissertation, Fakultät für Maschnenbau, RUB, Bochum, Germany (2016)
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…