Changizi, R.: Structural Analysis and Correlative Cathodoluminescence Investigations of Pr (doped) Niobates. Dissertation, Georessourcen und Materialtechnik, RWTH Aachen (2022)
Gomell, L.: Advancing the understanding of the microstructure-property relationship in non-toxic and cost-effective thermoelectric Heusler compounds. Dissertation, Fakultät für Georessourcen und Materialtechnik der RWTH Aachen, Germany (2022)
Abdellaoui, L.: Correlation of microstructures and thermal conductivity of the thermoelectric material Ag16.7Sb30Te53.3. Dissertation, Ruhr-Universität Bochum (2019)
Sysoltseva, M.: Characterization of aerosols and nanoparticles released during various indoor and outdoor human activities. Dissertation, RWTH Aachen University (2018)
Folger, A.: The Influence of Post-Growth Heat Treatments and Etching on the Nanostructure and Properties of Rutile TiO2 Nanowires. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Gleich, S.: Investigation of Sputtered Mo2BC Hard Coatings: Correlation of Nanostructure and Mechanical Properties. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Hengge, K.: Investigation of alternative catalyst and support materials and their effect on degradation in high-temperature polymer-electrolyte-membrane fuel cells. Dissertation, RWTH Aachen University, Aachen, Germany (2017)
Morsdorf, L.: Fundamentals of ferrous low-carbon lath martensite: from the as-quenched, to tempered and deformed states. Dissertation, RWTH Aachen, Aachen, Germany (2017)
Diehl, M.: High Resolution Crystal Plasticity Simulations. Dissertation, Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Aachen, Germany (2015)
Hill, B.: Scanning electron microscopy study of the microscale degradation mechanisms in polymer electrolyte fuel cells. Master, Heinrich-Heine-Universität Düsseldorf (2021)
Bueno Villoro, R.: Microstructure, thermal stability and defect phonon scattering in AgSbTe2 thermoelectrics. Master, Universitat Autònoma de Barcelona, Spain (2019)
Aymerich Armengol, R.: Structure-property relationship studies of Pt/TiO2 nanomaterials for electrochemical applications. Master, Universitat Autònoma de Barcelona, Spain (2019)
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…