Stechmann, G.; Zaefferer, S.; Konijnenberg, P. J.: Microstructural and Electronic Characterization of CdTe Thin Film Solar Cells: A Correlative SEM-Based Approach. IAMNano, Port Elizabeth, South Africa (2016)
Stechmann, G.; Zaefferer, S.: Microstructural and Electronic Characterization of CdTe Thin Film Solar Cells: A Correlative SEM-Based Approach. IAMNano, Hamburg, Germany (2015)
Zaefferer, S.; Mandal, S.; Stechmann, G.; Bozzolo, N.: Correlative measurement of the 5-parameter grain boundary character and its physical and chemical properties. RMS EBSD 2014, London, UK (2014)
Stechmann, G.: A Study on the Microstructure Formation Mechanisms and Functional Properties of CdTe Thin Film Solar Cells Using Correlative Electron Microscopy and Atomistic Simulations. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Stechmann, G.: Compatibility between Molten Salts and Materials in Concentrated Solar Power Plants. Diploma, École Nationale Supérieure de Chimie de Lille, Lille, France (2013)
Stechmann, G.: Crystallographic and Electronic Characterization of Grain Boundaries in Cd–Te Thin Film Solar Cell. Master, University of Lille I, University of Science and Technology, Lille, France (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…