Herbig, M.; Choi, P.; Raabe, D.: Combining Structural and Chemical Information on the nm Scale by Correlative TEM and APT Characterization. Euromat 2013, Sevilla, Spain (2013)
Gutiérrez-Urrutia, I.; Seol, J.-B.; Marceau, R. K. W.; Choi, P.; Raabe, D.: Multi-scale characterization of advanced structural steels: from the micro to the atomic-scale. 8th Pacific Rim International Congress on Advanced Materials and Processing (PRICM-8), Waikoloa, Hawai, USA (2013)
Herbig, M.; Choi, P.; Raabe, D.: Combining Structural and Chemical Information on the nm Scale by Correlative TEM and APT Characterization. Microscopy and Microanalysis 2013, Indianapolis, IN, USA (2013)
Li, Y. J.; Choi, P.; Herbig, M.; Kostka, A.; Goto, S.; Borchers, C.; Raabe, D.; Kirchheim, R.: Atomic Scale Understanding of 6.8 GPa Ultra-high Strength Pearlite. 8th Pacific Rim International Congress on Advanced Materials and Processing (PRICM-8), Waikoloa, HI, USA (2013)
Raabe, D.; Choi, P.; Herbig, M.; Li, Y.; Zaefferer, S.; Kirchheim, R.: Iron – Mythology and High Tech: From Electronic Understanding to Bulk Nanostructuring of 1 Billion Tons. Summer School 2013 on Functional Solids – FERRUM - organized by Leibniz University Hannover, Goslar, Germany (2013)
Choi, P.-P.: Characterization of internal interfaces in Cu(In,Ga)Se2 thin-film solar cells using correlative microscopy. IEEE – Photovoltaic Specialist Conference, Denver, CO, USA (2013)
Cojocaru-Mirédin, O.; Choi, P.; Würz, R.; Raabe, D.: Exploring the internal interfaces in Cu(In,Ga)Se2 thin-film solar cells at the atomic-scale. 2013 MRS Spring Meeting & Exhibit, San Francisco, CA, USA (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…