Zhang, S.; Mio, A.; Cagnoni, M.; Zhu, M.; Cojocaru-Mirédin, O.; Wuttig, M.; Scheu, C.: Valence EELS investigation on GeSexTe1-x phase change material. EDGE 2017: Enhanced Data Generated by Electrons, 8th International Workshop on Electron Energy Loss Spectroscopy and Related Techniques, Okuma, Okinawa, Japan (2017)
Koprek, A.; Cojocaru-Mirédin, O.; Freysoldt, C.; Würz, R.; Raabe, D.: Atomic scale investigation of the p-n Junction in CIGS based solar cells: correlation between cell efficiency and impurities. E-MRS 2014, Lille, France (2014)
Cojocaru-Mirédin, O.; Schwarz, T.; Choi, P.; Würz, R.; Raabe, D.: Characterization of Cu(In,Ga)Se2 grain boundaries using atom probe tomography. 2013 MRS Spring Meeting & Exhibit, San Francisco, CA, USA (2013)
Cojocaru-Mirédin, O.; Choi, P.; Wuerz, R.; Raabe, D.: Atomic-scale analysis of the p-n junction in CI(G)S thin-film solar cells. Euromat 2011, Montpellier, France (2011)
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…