Brognara, A.; Best, J. P.; Djemia, P.; Faurie, D.; Dehm, G.; Ghidelli, M.: Toward engineered thin film metallic glasses with large mechanical properties: effect of composition and nanostructure. Seminar at Laboratoire des Sciences des Procédés et des Matériaux (LSPM), Paris Nord University, Paris, France (2021)
Brognara, A.; Nasri, I. F. M. A.; Bricchi, B. R.; Li Bassi, A.; Gauchotte, C.; Ghidelli, M.; Lidgi-Guigui, N.: Detection of estradiol by a SERS sensor based on TiO2 covered with gold nanoparticles. Applied Nanotechnology and Nanoscience International Conference – ANNIC 2019, Paris, France (2019)
Brognara, A.; Best, J. P.; Djemia, P.; Faurie, D.; Ghidelli, M.; Dehm, G.: On the mechanical properties and thermal stability of ZrxCu100-x thin film metallic glasses with different compositions. Nanobrücken 2021 - Nanomechanical Testing Conference virtual event, Düsseldorf, Germany (2021)
Brognara, A.; Best, J. P.; Djemia, P.; Faurie, D.; Ghidelli, M.; Dehm, G.: Effect of composition on mechanical properties and thermal stability of ZrCu thin film metallic glasses. European Materials Research Society (E-MRS) Spring Meeting 2021, Virtual Conference, Strasbourg, France (2021)
Devulapalli, V.; Frommeyer, L.; Ghidelli, M.; Liebscher, C.; Dehm, G.: From epitaxially grown thin films to grain boundary analysis in Cu and Ti. International Workshop on Advanced and In-situ Microscopies of Functional Nanomaterials and Devices, IAMNano, Düsseldorf, Germany (2019)
Brognara, A.: Design of ZrCu thin film metallic glasses with tailored mechanical properties through control of composition and nanostructure. Dissertation, RUB Bochum, Bochum, Germany (2025)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
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.